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+ + Computation and Language 86 +
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+
+ + ☆ Long-Form Speech Generation with Spoken Language Models +
+
+
+
+
+
+
+
+ Se Jin Park, Julian Salazar, Aren Jansen, Keisuke Kinoshita, Yong Man Ro, RJ Skerry-Ryan
+
+
+ We consider the generative modeling of speech over multiple minutes, a
+requirement for long-form multimedia generation and audio-native voice
+assistants. However, current spoken language models struggle to generate
+plausible speech past tens of seconds, from high temporal resolution of speech
+tokens causing loss of coherence, to architectural issues with long-sequence
+training or extrapolation, to memory costs at inference time. With these
+considerations we propose SpeechSSM, the first speech language model to learn
+from and sample long-form spoken audio (e.g., 16 minutes of read or
+extemporaneous speech) in a single decoding session without text intermediates,
+based on recent advances in linear-time sequence modeling. Furthermore, to
+address growing challenges in spoken language evaluation, especially in this
+new long-form setting, we propose: new embedding-based and LLM-judged metrics;
+quality measurements over length and time; and a new benchmark for long-form
+speech processing and generation, LibriSpeech-Long. Speech samples and the
+dataset are released at
+https://google.github.io/tacotron/publications/speechssm/
+
+
+
+
+ + ☆ Exploring Embedding Priors in Prompt-Tuning for Improved + Interpretability and Control +
+ +
+ Prompt-Tuning is an efficient method for adapting pre-trained language models
+to new tasks with minimal computational overhead by modifying prompt
+embeddings. In this work, we investigate how crucial the phenomenon of
+embedding collapse, frequently observed in Prompt-Tuning, is for the final
+performance of the model. To address this question, we designed embedding
+priors and compared them with posteriors of the converged Soft and Deep
+Prompt-Tuning methods. Our findings suggest that priors strongly affect the
+position of the tuned embeddings, and models can effectively work with
+embeddings from different parts of activation spaces, including completely new
+regions. As the final Prompt-Tuning capabilities are limited, we hypothesize
+that controllable Prompt-Tuning posteriors may serve as a good starting point
+for tasks such as chain-of-thought (COT) distillation. Our experiments also
+show that generated trajectories are not localized in the activation space of
+the models. However, there are distinct clusters of activations for distant
+tasks (e.g., NLP and arithmetic), while activations between NLP tasks (e.g.,
+Question-Answering and MLM) lie in the same cluster. These observations raise
+questions about the importance of a single activation cluster for the
+generalization abilities of large language models.
+
+
+
+
+ + ☆ How Well Do LLMs Generate Code for Different Application Domains? + Benchmark and Evaluation +
+ +
+ Recently, an increasing number of AI-driven programming assistants powered by
+code LLMs have been integrated into various real-world software development
+environments, significantly boosting developer productivity. However, existing
+code generation benchmarks primarily focus on general-purpose scenarios,
+leaving the code generation performance of LLMs for specific application
+domains largely unknown. In this paper, we introduce a new benchmark,
+MultiCodeBench, to fill this gap. MultiCodeBench comprises 2,400 programming
+tasks, covering 12 popular software development domains and 15 programming
+languages. Specifically, we perform in-depth research to identify these 12
+application domains. Given that each domain may involve multiple technical
+frameworks, and that different frameworks present distinct challenges in the
+coding process, we categorize the commonly used frameworks and platforms within
+each domain. We then sample programming problems from GitHub repositories
+related to these subdomains. To ensure the quality of the tasks and mitigate
+data leakage issues, we invite annotators to rewrite the docstrings for each
+task in MultiCodeBench. Additionally, we build a static analysis-based
+dependency parsing tool to extract the dependencies in the ground truth for
+each task, enabling deeper performance analysis. Through extensive experiments
+on MultiCodeBench with eleven representative mainstream LLMs, we reveal the
+code generation performance of the LLMs across different application domains,
+providing practical insights for developers in downstream fields when selecting
+LLMs. Furthermore, we analyze the reasons behind the models' failures in
+completing software application development tasks, offering guidance for model
+developers to enhance domain-specific code generation capabilities.
+
+
+
+
+ + ☆ Zero-resource Speech Translation and Recognition with LLMs ICASSP 2025 +
+
+
+
+
+
+
+
+ Karel Mundnich, Xing Niu, Prashant Mathur, Srikanth Ronanki, Brady Houston, Veera Raghavendra Elluru, Nilaksh Das, Zejiang Hou, Goeric Huybrechts, Anshu Bhatia, Daniel Garcia-Romero, Kyu J. Han, Katrin Kirchhoff
+
+
+ Despite recent advancements in speech processing, zero-resource speech
+translation (ST) and automatic speech recognition (ASR) remain challenging
+problems. In this work, we propose to leverage a multilingual Large Language
+Model (LLM) to perform ST and ASR in languages for which the model has never
+seen paired audio-text data. We achieve this by using a pre-trained
+multilingual speech encoder, a multilingual LLM, and a lightweight adaptation
+module that maps the audio representations to the token embedding space of the
+LLM. We perform several experiments both in ST and ASR to understand how to
+best train the model and what data has the most impact on performance in
+previously unseen languages. In ST, our best model is capable to achieve BLEU
+scores over 23 in CoVoST2 for two previously unseen languages, while in ASR, we
+achieve WERs of up to 28.2\%. We finally show that the performance of our
+system is bounded by the ability of the LLM to output text in the desired
+language.
+
+
+
+ comment: ICASSP 2025, 5 pages, 2 figures, 2 tables
+
+
+
+ + ☆ Distilling Fine-grained Sentiment Understanding from Large Language + Models +
+
+
+
+
+
+
+
+ Yice Zhang, Guangyu Xie, Hongling Xu, Kaiheng Hou, Jianzhu Bao, Qianlong Wang, Shiwei Chen, Ruifeng Xu
+
+
+ Fine-grained sentiment analysis (FSA) aims to extract and summarize user
+opinions from vast opinionated text. Recent studies demonstrate that large
+language models (LLMs) possess exceptional sentiment understanding
+capabilities. However, directly deploying LLMs for FSA applications incurs high
+inference costs. Therefore, this paper investigates the distillation of
+fine-grained sentiment understanding from LLMs into small language models
+(SLMs). We prompt LLMs to examine and interpret the sentiments of given reviews
+and then utilize the generated content to pretrain SLMs. Additionally, we
+develop a comprehensive FSA benchmark to evaluate both SLMs and LLMs. Extensive
+experiments on this benchmark reveal that: (1) distillation significantly
+enhances the performance of SLMs in FSA tasks, achieving a 6.00\% improvement
+in $F_1$-score, and the distilled model can outperform Llama-2-7b with only
+220M parameters; (2) distillation equips SLMs with excellent zero-shot
+sentiment classification capabilities, enabling them to match or even exceed
+their teacher models. These results suggest that distillation from LLMs is a
+highly promising direction for FSA. We will release our code, data, and
+pretrained model weights at
+\url{https://github.com/HITSZ-HLT/FSA-Distillation}.
+
+
+
+
+ + ☆ Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard + of Safety and Capability +
+
+
+
+
+
+
+
+ Haonan Li, Xudong Han, Zenan Zhai, Honglin Mu, Hao Wang, Zhenxuan Zhang, Yilin Geng, Shom Lin, Renxi Wang, Artem Shelmanov, Xiangyu Qi, Yuxia Wang, Donghai Hong, Youliang Yuan, Meng Chen, Haoqin Tu, Fajri Koto, Tatsuki Kuribayashi, Cong Zeng, Rishabh Bhardwaj, Bingchen Zhao, Yawen Duan, Yi Liu, Emad A. Alghamdi, Yaodong Yang, Yinpeng Dong, Soujanya Poria, Pengfei Liu, Zhengzhong Liu, Xuguang Ren, Eduard Hovy, Iryna Gurevych, Preslav Nakov, Monojit Choudhury, Timothy Baldwin
+
+
+ To address this gap, we introduce Libra-Leaderboard, a comprehensive
+framework designed to rank LLMs through a balanced evaluation of performance
+and safety. Combining a dynamic leaderboard with an interactive LLM arena,
+Libra-Leaderboard encourages the joint optimization of capability and safety.
+Unlike traditional approaches that average performance and safety metrics,
+Libra-Leaderboard uses a distance-to-optimal-score method to calculate the
+overall rankings. This approach incentivizes models to achieve a balance rather
+than excelling in one dimension at the expense of some other ones. In the first
+release, Libra-Leaderboard evaluates 26 mainstream LLMs from 14 leading
+organizations, identifying critical safety challenges even in state-of-the-art
+models.
+
+
+
+
+ + ☆ Token-Budget-Aware LLM Reasoning +
+ +
+ Reasoning is critical for large language models (LLMs) to excel in a wide
+range of tasks. While methods like Chain-of-Thought (CoT) reasoning enhance LLM
+performance by decomposing problems into intermediate steps, they also incur
+significant overhead in token usage, leading to increased costs. We find that
+the reasoning process of current LLMs is unnecessarily lengthy and it can be
+compressed by including a reasonable token budget in the prompt, but the choice
+of token budget plays a crucial role in the actual compression effectiveness.
+We then propose a token-budget-aware LLM reasoning framework, which dynamically
+estimates token budgets for different problems based on reasoning complexity
+and uses the estimated token budgets to guide the reasoning process.
+Experiments show that our method effectively reduces token costs in CoT
+reasoning with only a slight performance reduction, offering a practical
+solution to balance efficiency and accuracy in LLM reasoning. Code:
+https://github.com/GeniusHTX/TALE.
+
+
+
+
+ + ☆ Consistency Checks for Language Model Forecasters ICLR 2025 +
+
+
+
+
+
+
+
+ Daniel Paleka, Abhimanyu Pallavi Sudhir, Alejandro Alvarez, Vineeth Bhat, Adam Shen, Evan Wang, Florian Tramèr
+
+
+ Forecasting is a task that is difficult to evaluate: the ground truth can
+only be known in the future. Recent work showing LLM forecasters rapidly
+approaching human-level performance begs the question: how can we benchmark and
+evaluate these forecasters instantaneously? Following the consistency check
+framework, we measure the performance of forecasters in terms of the
+consistency of their predictions on different logically-related questions. We
+propose a new, general consistency metric based on arbitrage: for example, if a
+forecasting AI illogically predicts that both the Democratic and Republican
+parties have 60% probability of winning the 2024 US presidential election, an
+arbitrageur can trade against the forecaster's predictions and make a profit.
+We build an automated evaluation system that generates a set of base questions,
+instantiates consistency checks from these questions, elicits the predictions
+of the forecaster, and measures the consistency of the predictions. We then
+build a standard, proper-scoring-rule forecasting benchmark, and show that our
+(instantaneous) consistency metrics correlate with LLM forecasters' ground
+truth Brier scores (which are only known in the future). We also release a
+consistency benchmark that resolves in 2028, providing a long-term evaluation
+tool for forecasting.
+
+
+
+ comment: 56 pages, 25 figures. Submitted to ICLR 2025
+
+
+
+ + ☆ Harnessing Large Language Models for Knowledge Graph Question Answering + via Adaptive Multi-Aspect Retrieval-Augmentation AAAI'2025 +
+
+
+
+
+
+
+
+ Derong Xu Xinhang Li, Ziheng Zhang, Zhenxi Lin, Zhihong Zhu, Zhi Zheng, Xian Wu, Xiangyu Zhao, Tong Xu, Enhong Chen
+
+
+ Large Language Models (LLMs) demonstrate remarkable capabilities, yet
+struggle with hallucination and outdated knowledge when tasked with complex
+knowledge reasoning, resulting in factually incorrect outputs. Previous studies
+have attempted to mitigate it by retrieving factual knowledge from large-scale
+knowledge graphs (KGs) to assist LLMs in logical reasoning and prediction of
+answers. However, this kind of approach often introduces noise and irrelevant
+data, especially in situations with extensive context from multiple knowledge
+aspects. In this way, LLM attention can be potentially mislead from question
+and relevant information. In our study, we introduce an Adaptive Multi-Aspect
+Retrieval-augmented over KGs (Amar) framework. This method retrieves knowledge
+including entities, relations, and subgraphs, and converts each piece of
+retrieved text into prompt embeddings. The Amar framework comprises two key
+sub-components: 1) a self-alignment module that aligns commonalities among
+entities, relations, and subgraphs to enhance retrieved text, thereby reducing
+noise interference; 2) a relevance gating module that employs a soft gate to
+learn the relevance score between question and multi-aspect retrieved data, to
+determine which information should be used to enhance LLMs' output, or even
+filtered altogether. Our method has achieved state-of-the-art performance on
+two common datasets, WebQSP and CWQ, showing a 1.9\% improvement in accuracy
+over its best competitor and a 6.6\% improvement in logical form generation
+over a method that directly uses retrieved text as context prompts. These
+results demonstrate the effectiveness of Amar in improving the reasoning of
+LLMs.
+
+
+
+ comment: Accepted by AAAI'2025
+
+
+
+ + ☆ Characterizations of Language Generation With Breadth +
+ +
+ We study language generation in the limit, introduced by Kleinberg and
+Mullainathan [KM24], building on classical works of Gold [Gol67] and Angluin
+[Ang79]. [KM24] proposed an algorithm that generates strings from any countable
+language collection in the limit. While their algorithm eventually outputs
+strings from the target language $K$, it sacrifices breadth, i.e., the ability
+to generate all strings in $K$. A key open question in [KM24] is whether this
+trade-off between consistency and breadth is inherrent.
+ Recent works proposed different notions of consistent generation with
+breadth. Kalavasis, Mehrotra, and Velegkas [KVM24] introduced three
+definitions: generation with exact breadth, approximate breadth, and
+unambiguous generation. Concurrently and independently, Charikar and Pabbaraju
+[CP24a] proposed exhaustive generation. Both works examined when generation
+with these notions of breadth is possible.
+ Building on [CP24a, KVM24], we fully characterize language generation for
+these notions and their natural combinations. For exact breadth, we provide an
+unconditional lower bound, removing a technical condition from [KVM24] and
+extending the result of [CP24a] that holds for specific collections of
+languages. We show that generation with exact breadth is characterized by
+Angluin's condition for identification. We further introduce a weaker version
+of Angluin's condition that tightly characterizes both approximate breadth and
+exhaustive generation, proving their equivalence. Additionally, we show that
+unambiguous generation is also characterized by Angluin's condition as a
+special case of a broader result. Finally, we strengthen [KVM24] by giving
+unconditional lower bounds for stable generators, showing that Angluin's
+condition characterizes the previous breadth notions for stable generators.
+This shows a separation between stable and unstable generation with approximate
+breadth.
+
+
+
+ comment: Abstract shortened to fix arXiv limit
+
+
+
+ + ☆ Think or Remember? Detecting and Directing LLMs Towards Memorization or + Generalization +
+
+
+
+
+
+
+
+ Yi-Fu Fu, Yu-Chieh Tu, Tzu-Ling Cheng, Cheng-Yu Lin, Yi-Ting Yang, Heng-Yi Liu, Keng-Te Liao, Da-Cheng Juan, Shou-De Lin
+
+
+ In this paper, we explore the foundational mechanisms of memorization and
+generalization in Large Language Models (LLMs), inspired by the functional
+specialization observed in the human brain. Our investigation serves as a case
+study leveraging specially designed datasets and experimental-scale LLMs to lay
+the groundwork for understanding these behaviors. Specifically, we aim to first
+enable LLMs to exhibit both memorization and generalization by training with
+the designed dataset, then (a) examine whether LLMs exhibit neuron-level
+spatial differentiation for memorization and generalization, (b) predict these
+behaviors using model internal representations, and (c) steer the behaviors
+through inference-time interventions. Our findings reveal that neuron-wise
+differentiation of memorization and generalization is observable in LLMs, and
+targeted interventions can successfully direct their behavior.
+
+
+
+
+ + ☆ Generating event descriptions under syntactic and semantic constraints +
+
+
+
+
+
+
+
+ Angela Cao, Faye Holt, Jonas Chan, Stephanie Richter, Lelia Glass, Aaron Steven White
+
+
+ With the goal of supporting scalable lexical semantic annotation, analysis,
+and theorizing, we conduct a comprehensive evaluation of different methods for
+generating event descriptions under both syntactic constraints -- e.g. desired
+clause structure -- and semantic constraints -- e.g. desired verb sense. We
+compare three different methods -- (i) manual generation by experts; (ii)
+sampling from a corpus annotated for syntactic and semantic information; and
+(iii) sampling from a language model (LM) conditioned on syntactic and semantic
+information -- along three dimensions of the generated event descriptions: (a)
+naturalness, (b) typicality, and (c) distinctiveness. We find that all methods
+reliably produce natural, typical, and distinctive event descriptions, but that
+manual generation continues to produce event descriptions that are more
+natural, typical, and distinctive than the automated generation methods. We
+conclude that the automated methods we consider produce event descriptions of
+sufficient quality for use in downstream annotation and analysis insofar as the
+methods used for this annotation and analysis are robust to a small amount of
+degradation in the resulting event descriptions.
+
+
+
+
+ + ☆ How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation + System? ACL +
+ +
+ Simultaneous speech-to-text translation (SimulST) translates source-language
+speech into target-language text concurrently with the speaker's speech,
+ensuring low latency for better user comprehension. Despite its intended
+application to unbounded speech, most research has focused on human
+pre-segmented speech, simplifying the task and overlooking significant
+challenges. This narrow focus, coupled with widespread terminological
+inconsistencies, is limiting the applicability of research outcomes to
+real-world applications, ultimately hindering progress in the field. Our
+extensive literature review of 110 papers not only reveals these critical
+issues in current research but also serves as the foundation for our key
+contributions. We 1) define the steps and core components of a SimulST system,
+proposing a standardized terminology and taxonomy; 2) conduct a thorough
+analysis of community trends, and 3) offer concrete recommendations and future
+directions to bridge the gaps in existing literature, from evaluation
+frameworks to system architectures, for advancing the field towards more
+realistic and effective SimulST solutions.
+
+
+
+ comment: Accepted at TACL
+
+
+
+ + ☆ Segment-Based Attention Masking for GPTs +
+ +
+ Modern Language Models (LMs) owe much of their success to masked causal
+attention, the backbone of Generative Pre-Trained Transformer (GPT) models.
+Although GPTs can process the entire user prompt at once, the causal masking is
+applied to all input tokens step-by-step, mimicking the generation process.
+This imposes an unnecessary constraint during the initial "prefill" phase when
+the model processes the input prompt and generates the internal representations
+before producing any output tokens. In this work, attention is masked based on
+the known block structure at the prefill phase, followed by the conventional
+token-by-token autoregressive process after that. For example, in a typical
+chat prompt, the system prompt is treated as one block, and the user prompt as
+the next one. Each of these is treated as a unit for the purpose of masking,
+such that the first tokens in each block can access the subsequent tokens in a
+non-causal manner. Then, the model answer is generated in the conventional
+causal manner. This Segment-by-Segment scheme entails no additional
+computational overhead. When integrating it into models such as Llama and Qwen,
+state-of-the-art performance is consistently achieved.
+
+
+
+
+ + ☆ Is Large Language Model Good at Triple Set Prediction? An Empirical + Study +
+ +
+ The core of the Knowledge Graph Completion (KGC) task is to predict and
+complete the missing relations or nodes in a KG. Common KGC tasks are mostly
+about inferring unknown elements with one or two elements being known in a
+triple. In comparison, the Triple Set Prediction (TSP) task is a more realistic
+knowledge graph completion task. It aims to predict all elements of unknown
+triples based on the information from known triples. In recent years, large
+language models (LLMs) have exhibited significant advancements in language
+comprehension, demonstrating considerable potential for KGC tasks. However, the
+potential of LLM on the TSP task has not yet to be investigated. Thus in this
+paper we proposed a new framework to explore the strengths and limitations of
+LLM in the TSP task. Specifically, the framework consists of LLM-based rule
+mining and LLM-based triple set prediction. The relation list of KG embedded
+within rich semantic information is first leveraged to prompt LLM in the
+generation of rules. This process is both efficient and independent of
+statistical information, making it easier to mine effective and realistic
+rules. For each subgraph, the specified rule is applied in conjunction with the
+relevant triples within that subgraph to guide the LLM in predicting the
+missing triples. Subsequently, the predictions from all subgraphs are
+consolidated to derive the complete set of predicted triples on KG. Finally,
+the method is evaluated on the relatively complete CFamily dataset. The
+experimental results indicate that when LLMs are required to adhere to a large
+amount of factual knowledge to predict missing triples, significant
+hallucinations occurs, leading to a noticeable decline in performance. To
+further explore the causes of this phenomenon, this paper presents a
+comprehensive analysis supported by a detailed case study.
+
+
+
+
+ + ☆ Unlocking the Potential of Multiple BERT Models for Bangla Question + Answering in NCTB Textbooks +
+
+
+
+
+
+
+
+ Abdullah Khondoker, Enam Ahmed Taufik, Md Iftekhar Islam Tashik, S M Ishtiak mahmud, Antara Firoz Parsa
+
+
+ Evaluating text comprehension in educational settings is critical for
+understanding student performance and improving curricular effectiveness. This
+study investigates the capability of state-of-the-art language models-RoBERTa
+Base, Bangla-BERT, and BERT Base-in automatically assessing Bangla
+passage-based question-answering from the National Curriculum and Textbook
+Board (NCTB) textbooks for classes 6-10. A dataset of approximately 3,000
+Bangla passage-based question-answering instances was compiled, and the models
+were evaluated using F1 Score and Exact Match (EM) metrics across various
+hyperparameter configurations. Our findings revealed that Bangla-BERT
+consistently outperformed the other models, achieving the highest F1 (0.75) and
+EM (0.53) scores, particularly with smaller batch sizes, the inclusion of stop
+words, and a moderate learning rate. In contrast, RoBERTa Base demonstrated the
+weakest performance, with the lowest F1 (0.19) and EM (0.27) scores under
+certain configurations. The results underscore the importance of fine-tuning
+hyperparameters for optimizing model performance and highlight the potential of
+machine learning models in evaluating text comprehension in educational
+contexts. However, limitations such as dataset size, spelling inconsistencies,
+and computational constraints emphasize the need for further research to
+enhance the robustness and applicability of these models. This study lays the
+groundwork for the future development of automated evaluation systems in
+educational institutions, providing critical insights into model performance in
+the context of Bangla text comprehension.
+
+
+
+
+ + ☆ GeAR: Graph-enhanced Agent for Retrieval-augmented Generation +
+
+
+
+
+
+
+
+ Zhili Shen, Chenxin Diao, Pavlos Vougiouklis, Pascual Merita, Shriram Piramanayagam, Damien Graux, Dandan Tu, Zeren Jiang, Ruofei Lai, Yang Ren, Jeff Z. Pan
+
+
+ Retrieval-augmented generation systems rely on effective document retrieval
+capabilities. By design, conventional sparse or dense retrievers face
+challenges in multi-hop retrieval scenarios. In this paper, we present GeAR,
+which advances RAG performance through two key innovations: (i) graph
+expansion, which enhances any conventional base retriever, such as BM25, and
+(ii) an agent framework that incorporates graph expansion. Our evaluation
+demonstrates GeAR's superior retrieval performance on three multi-hop question
+answering datasets. Additionally, our system achieves state-of-the-art results
+with improvements exceeding 10% on the challenging MuSiQue dataset, while
+requiring fewer tokens and iterations compared to other multi-step retrieval
+systems.
+
+
+
+
+ + ☆ Explainable Multi-Modal Data Exploration in Natural Language via LLM + Agent +
+ +
+ International enterprises, organizations, or hospitals collect large amounts
+of multi-modal data stored in databases, text documents, images, and videos.
+While there has been recent progress in the separate fields of multi-modal data
+exploration as well as in database systems that automatically translate natural
+language questions to database query languages, the research challenge of
+querying database systems combined with other unstructured modalities such as
+images in natural language is widely unexplored.
+ In this paper, we propose XMODE - a system that enables explainable,
+multi-modal data exploration in natural language. Our approach is based on the
+following research contributions: (1) Our system is inspired by a real-world
+use case that enables users to explore multi-modal information systems. (2)
+XMODE leverages a LLM-based agentic AI framework to decompose a natural
+language question into subtasks such as text-to-SQL generation and image
+analysis. (3) Experimental results on multi-modal datasets over relational data
+and images demonstrate that our system outperforms state-of-the-art multi-modal
+exploration systems, excelling not only in accuracy but also in various
+performance metrics such as query latency, API costs, planning efficiency, and
+explanation quality, thanks to the more effective utilization of the reasoning
+capabilities of LLMs.
+
+
+
+
+ + ☆ LongDocURL: a Comprehensive Multimodal Long Document Benchmark + Integrating Understanding, Reasoning, and Locating +
+
+
+
+
+
+
+
+ Chao Deng, Jiale Yuan, Pi Bu, Peijie Wang, Zhong-Zhi Li, Jian Xu, Xiao-Hui Li, Yuan Gao, Jun Song, Bo Zheng, Cheng-Lin Liu
+
+
+ Large vision language models (LVLMs) have improved the document understanding
+capabilities remarkably, enabling the handling of complex document elements,
+longer contexts, and a wider range of tasks. However, existing document
+understanding benchmarks have been limited to handling only a small number of
+pages and fail to provide a comprehensive analysis of layout elements locating.
+In this paper, we first define three primary task categories: Long Document
+Understanding, numerical Reasoning, and cross-element Locating, and then
+propose a comprehensive benchmark, LongDocURL, integrating above three primary
+tasks and comprising 20 sub-tasks categorized based on different primary tasks
+and answer evidences. Furthermore, we develop a semi-automated construction
+pipeline and collect 2,325 high-quality question-answering pairs, covering more
+than 33,000 pages of documents, significantly outperforming existing
+benchmarks. Subsequently, we conduct comprehensive evaluation experiments on
+both open-source and closed-source models across 26 different configurations,
+revealing critical performance gaps in this field.
+
+
+
+
+ + ☆ Multilingual Mathematical Reasoning: Advancing Open-Source LLMs in Hindi + and English AAAI 2025 +
+
+
+
+
+
+
+
+ Avinash Anand, Kritarth Prasad, Chhavi Kirtani, Ashwin R Nair, Manvendra Kumar Nema, Raj Jaiswal, Rajiv Ratn Shah
+
+
+ Large Language Models (LLMs) excel in linguistic tasks but struggle with
+mathematical reasoning, particularly in non English languages like Hindi. This
+research aims to enhance the mathematical reasoning skills of smaller, resource
+efficient open-source LLMs in both Hindi and English. We evaluate models like
+OpenHathi 7B, LLaMA-2 7B, WizardMath 7B, Mistral 7B, LLeMMa 7B, MAmmoTH 7B,
+Gemini Pro, and GPT-4 using zero-shot, few-shot chain-of-thought (CoT) methods,
+and supervised fine-tuning. Our approach incorporates curriculum learning,
+progressively training models on increasingly difficult problems, a novel
+Decomposition Strategy to simplify complex arithmetic operations, and a
+Structured Solution Design that divides solutions into phases. Our experiments
+result in notable performance enhancements. WizardMath 7B exceeds Gemini's
+accuracy on English datasets by +6% and matches Gemini's performance on Hindi
+datasets. Adopting a bilingual approach that combines English and Hindi samples
+achieves results comparable to individual language models, demonstrating the
+capability to learn mathematical reasoning in both languages. This research
+highlights the potential for improving mathematical reasoning in open-source
+LLMs.
+
+
+
+ comment: Accepted at AAAI 2025
+
+
+
+ + ☆ ChaI-TeA: A Benchmark for Evaluating Autocompletion of Interactions with + LLM-based Chatbots +
+
+
+
+
+
+
+
+ Shani Goren, Oren Kalinsky, Tomer Stav, Yuri Rapoport, Yaron Fairstein, Ram Yazdy, Nachshon Cohen, Alexander Libov, Guy Kushilevitz
+
+
+ The rise of LLMs has deflected a growing portion of human-computer
+interactions towards LLM-based chatbots. The remarkable abilities of these
+models allow users to interact using long, diverse natural language text
+covering a wide range of topics and styles. Phrasing these messages is a time
+and effort consuming task, calling for an autocomplete solution to assist
+users. We introduce the task of chatbot interaction autocomplete. We present
+ChaI-TeA: CHat InTEraction Autocomplete; An autcomplete evaluation framework
+for LLM-based chatbot interactions. The framework includes a formal definition
+of the task, coupled with suitable datasets and metrics. We use the framework
+to evaluate After formally defining the task along with suitable datasets and
+metrics, we test 9 models on the defined auto completion task, finding that
+while current off-the-shelf models perform fairly, there is still much room for
+improvement, mainly in ranking of the generated suggestions. We provide
+insights for practitioners working on this task and open new research
+directions for researchers in the field. We release our framework to serve as a
+foundation for future research.
+
+
+
+
+ + ☆ Bidirectional Topic Matching: Quantifying Thematic Overlap Between + Corpora Through Topic Modelling +
+ +
+ This study introduces Bidirectional Topic Matching (BTM), a novel method for
+cross-corpus topic modeling that quantifies thematic overlap and divergence
+between corpora. BTM is a flexible framework that can incorporate various topic
+modeling approaches, including BERTopic, Top2Vec, and Latent Dirichlet
+Allocation (LDA). BTM employs a dual-model approach, training separate topic
+models for each corpus and applying them reciprocally to enable comprehensive
+cross-corpus comparisons. This methodology facilitates the identification of
+shared themes and unique topics, providing nuanced insights into thematic
+relationships. Validation against cosine similarity-based methods demonstrates
+the robustness of BTM, with strong agreement metrics and distinct advantages in
+handling outlier topics. A case study on climate news articles showcases BTM's
+utility, revealing significant thematic overlaps and distinctions between
+corpora focused on climate change and climate action. BTM's flexibility and
+precision make it a valuable tool for diverse applications, from political
+discourse analysis to interdisciplinary studies. By integrating shared and
+unique topic analyses, BTM offers a comprehensive framework for exploring
+thematic relationships, with potential extensions to multilingual and dynamic
+datasets. This work highlights BTM's methodological contributions and its
+capacity to advance discourse analysis across various domains.
+
+
+
+ comment: 12 pages, 4 figures
+
+
+
+ + ☆ Towards Global AI Inclusivity: A Large-Scale Multilingual Terminology + Dataset +
+
+
+
+
+
+
+
+ Jiarui Liu, Iman Ouzzani, Wenkai Li, Lechen Zhang, Tianyue Ou, Houda Bouamor, Zhijing Jin, Mona Diab
+
+
+ The field of machine translation has achieved significant advancements, yet
+domain-specific terminology translation, particularly in AI, remains
+challenging. We introduced GIST, a large-scale multilingual AI terminology
+dataset containing 5K terms extracted from top AI conference papers spanning
+2000 to 2023. The terms were translated into Arabic, Chinese, French, Japanese,
+and Russian using a hybrid framework that combines LLMs for extraction with
+human expertise for translation. The dataset's quality was benchmarked against
+existing resources, demonstrating superior translation accuracy through
+crowdsourced evaluation. GIST was integrated into translation workflows using
+post-translation refinement methods that required no retraining, where LLM
+prompting consistently improved BLEU and COMET scores. A web demonstration on
+the ACL Anthology platform highlights its practical application, showcasing
+improved accessibility for non-English speakers. This work aims to address
+critical gaps in AI terminology resources and fosters global inclusivity and
+collaboration in AI research.
+
+
+
+
+ + ☆ Extracting triples from dialogues for conversational social agents +
+ +
+ Obtaining an explicit understanding of communication within a Hybrid
+Intelligence collaboration is essential to create controllable and transparent
+agents. In this paper, we describe a number of Natural Language Understanding
+models that extract explicit symbolic triples from social conversation. Triple
+extraction has mostly been developed and tested for Knowledge Base Completion
+using Wikipedia text and data for training and testing. However, social
+conversation is very different as a genre in which interlocutors exchange
+information in sequences of utterances that involve statements, questions, and
+answers. Phenomena such as co-reference, ellipsis, coordination, and implicit
+and explicit negation or confirmation are more prominent in conversation than
+in Wikipedia text. We therefore describe an attempt to fill this gap by
+releasing data sets for training and testing triple extraction from social
+conversation. We also created five triple extraction models and tested them in
+our evaluation data. The highest precision is 51.14 for complete triples and
+69.32 for triple elements when tested on single utterances. However, scores for
+conversational triples that span multiple turns are much lower, showing that
+extracting knowledge from true conversational data is much more challenging.
+
+
+
+
+ + ☆ Multi-Agents Based on Large Language Models for Knowledge-based Visual + Question Answering +
+ +
+ Large Language Models (LLMs) have achieved impressive results in
+knowledge-based Visual Question Answering (VQA). However existing methods still
+have challenges: the inability to use external tools autonomously, and the
+inability to work in teams. Humans tend to know whether they need to use
+external tools when they encounter a new question, e.g., they tend to be able
+to give a direct answer to a familiar question, whereas they tend to use tools
+such as search engines when they encounter an unfamiliar question. In addition,
+humans also tend to collaborate and discuss with others to get better answers.
+Inspired by this, we propose the multi-agent voting framework. We design three
+LLM-based agents that simulate different levels of staff in a team, and assign
+the available tools according to the levels. Each agent provides the
+corresponding answer, and finally all the answers provided by the agents are
+voted to get the final answer. Experiments on OK-VQA and A-OKVQA show that our
+approach outperforms other baselines by 2.2 and 1.0, respectively.
+
+
+
+
+ + ☆ M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models +
+
+
+
+
+
+
+
+ Jiaxin Guo, Daimeng Wei, Yuanchang Luo, Shimin Tao, Hengchao Shang, Zongyao Li, Shaojun Li, Jinlong Yang, Zhanglin Wu, Zhiqiang Rao, Hao Yang
+
+
+ With the widespread application of Large Language Models (LLMs) in the field
+of Natural Language Processing (NLP), enhancing their performance has become a
+research hotspot. This paper presents a novel multi-prompt ensemble decoding
+approach designed to bolster the generation quality of LLMs by leveraging the
+aggregation of outcomes from multiple prompts. Given a unique input $X$, we
+submit $n$ variations of prompts with $X$ to LLMs in batch mode to decode and
+derive probability distributions. For each token prediction, we calculate the
+ensemble probability by averaging the $n$ probability distributions within the
+batch, utilizing this aggregated probability to generate the token. This
+technique is dubbed Inner-Batch Ensemble. To facilitate efficient batch
+inference, we implement a Left-Padding strategy to maintain uniform input
+lengths across the n prompts. Through extensive experimentation on diverse NLP
+tasks, including machine translation, code generation, and text simplification,
+we demonstrate the efficacy of our method in enhancing LLM performance. The
+results show substantial improvements in BLEU scores, pass@$k$ rates, and LENS
+metrics over conventional methods.
+
+
+
+
+ + ☆ DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation +
+
+
+
+
+
+
+
+ Junyi Lu, Xiaojia Li, Zihan Hua, Lei Yu, Shiqi Cheng, Li Yang, Fengjun Zhang, Chun Zuo
+
+
+ Code review is a vital but demanding aspect of software development,
+generating significant interest in automating review comments. Traditional
+evaluation methods for these comments, primarily based on text similarity, face
+two major challenges: inconsistent reliability of human-authored comments in
+open-source projects and the weak correlation of text similarity with
+objectives like enhancing code quality and detecting defects.
+ This study empirically analyzes benchmark comments using a novel set of
+criteria informed by prior research and developer interviews. We then similarly
+revisit the evaluation of existing methodologies. Our evaluation framework,
+DeepCRCEval, integrates human evaluators and Large Language Models (LLMs) for a
+comprehensive reassessment of current techniques based on the criteria set.
+Besides, we also introduce an innovative and efficient baseline, LLM-Reviewer,
+leveraging the few-shot learning capabilities of LLMs for a target-oriented
+comparison.
+ Our research highlights the limitations of text similarity metrics, finding
+that less than 10% of benchmark comments are high quality for automation. In
+contrast, DeepCRCEval effectively distinguishes between high and low-quality
+comments, proving to be a more reliable evaluation mechanism. Incorporating LLM
+evaluators into DeepCRCEval significantly boosts efficiency, reducing time and
+cost by 88.78% and 90.32%, respectively. Furthermore, LLM-Reviewer demonstrates
+significant potential of focusing task real targets in comment generation.
+
+
+
+ comment: Accepted to the 28th International Conference on Fundamental
+ Approaches to Software Engineering (FASE 2025), part of the 28th European
+ Joint Conferences on Theory and Practice of Software (ETAPS 2025)
+
+
+
+ + ☆ GenAI Content Detection Task 2: AI vs. Human -- Academic Essay + Authenticity Challenge +
+
+
+
+
+
+
+
+ Shammur Absar Chowdhury, Hind Almerekhi, Mucahid Kutlu, Kaan Efe Keles, Fatema Ahmad, Tasnim Mohiuddin, George Mikros, Firoj Alam
+
+
+ This paper presents a comprehensive overview of the first edition of the
+Academic Essay Authenticity Challenge, organized as part of the GenAI Content
+Detection shared tasks collocated with COLING 2025. This challenge focuses on
+detecting machine-generated vs. human-authored essays for academic purposes.
+The task is defined as follows: "Given an essay, identify whether it is
+generated by a machine or authored by a human.'' The challenge involves two
+languages: English and Arabic. During the evaluation phase, 25 teams submitted
+systems for English and 21 teams for Arabic, reflecting substantial interest in
+the task. Finally, seven teams submitted system description papers. The
+majority of submissions utilized fine-tuned transformer-based models, with one
+team employing Large Language Models (LLMs) such as Llama 2 and Llama 3. This
+paper outlines the task formulation, details the dataset construction process,
+and explains the evaluation framework. Additionally, we present a summary of
+the approaches adopted by participating teams. Nearly all submitted systems
+outperformed the n-gram-based baseline, with the top-performing systems
+achieving F1 scores exceeding 0.98 for both languages, indicating significant
+progress in the detection of machine-generated text.
+
+
+
+ comment: AI Generated Content, Academic Essay, LLMs, Arabic, English
+
+
+
+ + ☆ Investigating Large Language Models for Code Vulnerability Detection: An + Experimental Study +
+
+
+
+
+
+
+
+ Xuefeng Jiang, Lvhua Wu, Sheng Sun, Jia Li, Jingjing Xue, Yuwei Wang, Tingting Wu, Min Liu
+
+
+ Code vulnerability detection (CVD) is essential for addressing and preventing
+system security issues, playing a crucial role in ensuring software security.
+Previous learning-based vulnerability detection methods rely on either
+fine-tuning medium-size sequence models or training smaller neural networks
+from scratch. Recent advancements in large pre-trained language models (LLMs)
+have showcased remarkable capabilities in various code intelligence tasks
+including code understanding and generation. However, the effectiveness of LLMs
+in detecting code vulnerabilities is largely under-explored. This work aims to
+investigate the gap by fine-tuning LLMs for the CVD task, involving four
+widely-used open-source LLMs. We also implement other five previous graph-based
+or medium-size sequence models for comparison. Experiments are conducted on
+five commonly-used CVD datasets, including both the part of short samples and
+long samples. In addition, we conduct quantitative experiments to investigate
+the class imbalance issue and the model's performance on samples of different
+lengths, which are rarely studied in previous works. To better facilitate
+communities, we open-source all codes and resources of this study in
+https://github.com/SakiRinn/LLM4CVD and
+https://huggingface.co/datasets/xuefen/VulResource.
+
+
+
+ comment: Under Review
+
+
+
+ + ☆ ICM-Assistant: Instruction-tuning Multimodal Large Language Models for + Rule-based Explainable Image Content Moderation AAAI 2025 +
+
+
+
+
+
+
+
+ Mengyang Wu, Yuzhi Zhao, Jialun Cao, Mingjie Xu, Zhongming Jiang, Xuehui Wang, Qinbin Li, Guangneng Hu, Shengchao Qin, Chi-Wing Fu
+
+
+ Controversial contents largely inundate the Internet, infringing various
+cultural norms and child protection standards. Traditional Image Content
+Moderation (ICM) models fall short in producing precise moderation decisions
+for diverse standards, while recent multimodal large language models (MLLMs),
+when adopted to general rule-based ICM, often produce classification and
+explanation results that are inconsistent with human moderators. Aiming at
+flexible, explainable, and accurate ICM, we design a novel rule-based dataset
+generation pipeline, decomposing concise human-defined rules and leveraging
+well-designed multi-stage prompts to enrich short explicit image annotations.
+Our ICM-Instruct dataset includes detailed moderation explanation and
+moderation Q-A pairs. Built upon it, we create our ICM-Assistant model in the
+framework of rule-based ICM, making it readily applicable in real practice. Our
+ICM-Assistant model demonstrates exceptional performance and flexibility.
+Specifically, it significantly outperforms existing approaches on various
+sources, improving both the moderation classification (36.8\% on average) and
+moderation explanation quality (26.6\% on average) consistently over existing
+MLLMs. Code/Data is available at https://github.com/zhaoyuzhi/ICM-Assistant.
+
+
+
+ comment: AAAI 2025
+
+
+
+ + ☆ Robustness-aware Automatic Prompt Optimization +
+ +
+ The performance of Large Language Models (LLMs) is based on the quality of
+the prompts and the semantic and structural integrity information of the input
+data. However, current prompt generation methods primarily focus on generating
+prompts for clean input data, often overlooking the impact of perturbed inputs
+on prompt performance. To address this limitation, we propose BATprompt (By
+Adversarial Training prompt), a novel method for prompt generation designed to
+withstand input perturbations (such as typos in the input). Inspired by
+adversarial training techniques, BATprompt demonstrates strong performance on a
+variety of perturbed tasks through a two-step process: adversarial perturbation
+and iterative optimization on unperturbed input via LLM. Unlike conventional
+adversarial attack methods, BATprompt avoids reliance on real gradients or
+model parameters. Instead, it leverages the advanced reasoning, language
+understanding and self reflection capabilities of LLMs to simulate gradients,
+guiding the generation of adversarial perturbations and optimizing prompt
+performance. In our experiments, we evaluate BATprompt on multiple datasets
+across both language understanding and generation tasks. The results indicate
+that BATprompt outperforms existing prompt generation methods, delivering
+superior robustness and performance under diverse perturbation scenarios.
+
+
+
+
+ + ☆ VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics + Manipulation with Long-Horizon Reasoning Tasks +
+
+
+
+
+
+
+
+ Shiduo Zhang, Zhe Xu, Peiju Liu, Xiaopeng Yu, Yuan Li, Qinghui Gao, Zhaoye Fei, Zhangyue Yin, Zuxuan Wu, Yu-Gang Jiang, Xipeng Qiu
+
+
+ General-purposed embodied agents are designed to understand the users'
+natural instructions or intentions and act precisely to complete universal
+tasks. Recently, methods based on foundation models especially
+Vision-Language-Action models (VLAs) have shown a substantial potential to
+solve language-conditioned manipulation (LCM) tasks well. However, existing
+benchmarks do not adequately meet the needs of VLAs and relative algorithms. To
+better define such general-purpose tasks in the context of LLMs and advance the
+research in VLAs, we present VLABench, an open-source benchmark for evaluating
+universal LCM task learning. VLABench provides 100 carefully designed
+categories of tasks, with strong randomization in each category of task and a
+total of 2000+ objects. VLABench stands out from previous benchmarks in four
+key aspects: 1) tasks requiring world knowledge and common sense transfer, 2)
+natural language instructions with implicit human intentions rather than
+templates, 3) long-horizon tasks demanding multi-step reasoning, and 4)
+evaluation of both action policies and language model capabilities. The
+benchmark assesses multiple competencies including understanding of
+mesh\&texture, spatial relationship, semantic instruction, physical laws,
+knowledge transfer and reasoning, etc. To support the downstream finetuning, we
+provide high-quality training data collected via an automated framework
+incorporating heuristic skills and prior information. The experimental results
+indicate that both the current state-of-the-art pretrained VLAs and the
+workflow based on VLMs face challenges in our tasks.
+
+
+
+
+ + ☆ An Analysis on Automated Metrics for Evaluating Japanese-English Chat + Translation +
+ +
+ This paper analyses how traditional baseline metrics, such as BLEU and TER,
+and neural-based methods, such as BERTScore and COMET, score several NMT models
+performance on chat translation and how these metrics perform when compared to
+human-annotated scores. The results show that for ranking NMT models in chat
+translations, all metrics seem consistent in deciding which model outperforms
+the others. This implies that traditional baseline metrics, which are faster
+and simpler to use, can still be helpful. On the other hand, when it comes to
+better correlation with human judgment, neural-based metrics outperform
+traditional metrics, with COMET achieving the highest correlation with the
+human-annotated score on a chat translation. However, we show that even the
+best metric struggles when scoring English translations from sentences with
+anaphoric zero-pronoun in Japanese.
+
+
+
+ comment: Accepted at the 29th Annual Meeting of the Association for Natural
+ Language Processing (NLP2023). Published version available at
+ https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/A8-1.pdf
+
+
+
+ + ☆ On the Applicability of Zero-Shot Cross-Lingual Transfer Learning for + Sentiment Classification in Distant Language Pairs +
+ +
+ This research explores the applicability of cross-lingual transfer learning
+from English to Japanese and Indonesian using the XLM-R pre-trained model. The
+results are compared with several previous works, either by models using a
+similar zero-shot approach or a fully-supervised approach, to provide an
+overview of the zero-shot transfer learning approach's capability using XLM-R
+in comparison with existing models. Our models achieve the best result in one
+Japanese dataset and comparable results in other datasets in Japanese and
+Indonesian languages without being trained using the target language.
+Furthermore, the results suggest that it is possible to train a multi-lingual
+model, instead of one model for each language, and achieve promising results.
+
+
+
+ comment: Accepted at the 28th Annual Meeting of the Association for Natural
+ Language Processing (NLP2022). Published version available at
+ https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/A6-1.pdf
+
+
+
+ + ☆ Survey of Pseudonymization, Abstractive Summarization & Spell Checker + for Hindi and Marathi +
+
+
+
+
+
+
+
+ Rasika Ransing, Mohammed Amaan Dhamaskar, Ayush Rajpurohit, Amey Dhoke, Sanket Dalvi
+
+
+ India's vast linguistic diversity presents unique challenges and
+opportunities for technological advancement, especially in the realm of Natural
+Language Processing (NLP). While there has been significant progress in NLP
+applications for widely spoken languages, the regional languages of India, such
+as Marathi and Hindi, remain underserved. Research in the field of NLP for
+Indian regional languages is at a formative stage and holds immense
+significance. The paper aims to build a platform which enables the user to use
+various features like text anonymization, abstractive text summarization and
+spell checking in English, Hindi and Marathi language. The aim of these tools
+is to serve enterprise and consumer clients who predominantly use Indian
+Regional Languages.
+
+
+
+
+ + ☆ scReader: Prompting Large Language Models to Interpret scRNA-seq Data ICDM 2024 +
+ +
+ Large language models (LLMs) have demonstrated remarkable advancements,
+primarily due to their capabilities in modeling the hidden relationships within
+text sequences. This innovation presents a unique opportunity in the field of
+life sciences, where vast collections of single-cell omics data from multiple
+species provide a foundation for training foundational models. However, the
+challenge lies in the disparity of data scales across different species,
+hindering the development of a comprehensive model for interpreting genetic
+data across diverse organisms. In this study, we propose an innovative hybrid
+approach that integrates the general knowledge capabilities of LLMs with
+domain-specific representation models for single-cell omics data
+interpretation. We begin by focusing on genes as the fundamental unit of
+representation. Gene representations are initialized using functional
+descriptions, leveraging the strengths of mature language models such as
+LLaMA-2. By inputting single-cell gene-level expression data with prompts, we
+effectively model cellular representations based on the differential expression
+levels of genes across various species and cell types. In the experiments, we
+constructed developmental cells from humans and mice, specifically targeting
+cells that are challenging to annotate. We evaluated our methodology through
+basic tasks such as cell annotation and visualization analysis. The results
+demonstrate the efficacy of our approach compared to other methods using LLMs,
+highlighting significant improvements in accuracy and interoperability. Our
+hybrid approach enhances the representation of single-cell data and offers a
+robust framework for future research in cross-species genetic analysis.
+
+
+
+ comment: 8 pages, Accepted by ICDM 2024
+
+
+
+ + ☆ GeneSUM: Large Language Model-based Gene Summary Extraction +
+
+
+
+
+
+
+
+ Zhijian Chen, Chuan Hu, Min Wu, Qingqing Long, Xuezhi Wang, Yuanchun Zhou, Meng Xiao
+
+
+ Emerging topics in biomedical research are continuously expanding, providing
+a wealth of information about genes and their function. This rapid
+proliferation of knowledge presents unprecedented opportunities for scientific
+discovery and formidable challenges for researchers striving to keep abreast of
+the latest advancements. One significant challenge is navigating the vast
+corpus of literature to extract vital gene-related information, a
+time-consuming and cumbersome task. To enhance the efficiency of this process,
+it is crucial to address several key challenges: (1) the overwhelming volume of
+literature, (2) the complexity of gene functions, and (3) the automated
+integration and generation. In response, we propose GeneSUM, a two-stage
+automated gene summary extractor utilizing a large language model (LLM). Our
+approach retrieves and eliminates redundancy of target gene literature and then
+fine-tunes the LLM to refine and streamline the summarization process. We
+conducted extensive experiments to validate the efficacy of our proposed
+framework. The results demonstrate that LLM significantly enhances the
+integration of gene-specific information, allowing more efficient
+decision-making in ongoing research.
+
+
+
+ comment: 7 pages, Accepted by BIBM 2024
+
+
+
+ + ☆ CoAM: Corpus of All-Type Multiword Expressions +
+
+
+
+
+
+
+
+ Yusuke Ide, Joshua Tanner, Adam Nohejl, Jacob Hoffman, Justin Vasselli, Hidetaka Kamigaito, Taro Watanabe
+
+
+ Multiword expressions (MWEs) refer to idiomatic sequences of multiple words.
+MWE identification, i.e., detecting MWEs in text, can play a key role in
+downstream tasks such as machine translation. Existing datasets for MWE
+identification are inconsistently annotated, limited to a single type of MWE,
+or limited in size. To enable reliable and comprehensive evaluation, we created
+CoAM: Corpus of All-Type Multiword Expressions, a dataset of 1.3K sentences
+constructed through a multi-step process to enhance data quality consisting of
+human annotation, human review, and automated consistency checking. MWEs in
+CoAM are tagged with MWE types, such as Noun and Verb, to enable fine-grained
+error analysis. Annotations for CoAM were collected using a new interface
+created with our interface generator, which allows easy and flexible annotation
+of MWEs in any form, including discontinuous ones. Through experiments using
+CoAM, we find that a fine-tuned large language model outperforms the current
+state-of-the-art approach for MWE identification. Furthermore, analysis using
+our MWE type tagged data reveals that Verb MWEs are easier than Noun MWEs to
+identify across approaches.
+
+
+
+
+ + ☆ Are We in the AI-Generated Text World Already? Quantifying and + Monitoring AIGT on Social Media +
+
+
+
+
+
+
+
+ Zhen Sun, Zongmin Zhang, Xinyue Shen, Ziyi Zhang, Yule Liu, Michael Backes, Yang Zhang, Xinlei He
+
+
+ Social media platforms are experiencing a growing presence of AI-Generated
+Texts (AIGTs). However, the misuse of AIGTs could have profound implications
+for public opinion, such as spreading misinformation and manipulating
+narratives. Despite its importance, a systematic study to assess the prevalence
+of AIGTs on social media is still lacking. To address this gap, this paper aims
+to quantify, monitor, and analyze the AIGTs on online social media platforms.
+We first collect a dataset (SM-D) with around 2.4M posts from 3 major social
+media platforms: Medium, Quora, and Reddit. Then, we construct a diverse
+dataset (AIGTBench) to train and evaluate AIGT detectors. AIGTBench combines
+popular open-source datasets and our AIGT datasets generated from social media
+texts by 12 LLMs, serving as a benchmark for evaluating mainstream detectors.
+With this setup, we identify the best-performing detector (OSM-Det). We then
+apply OSM-Det to SM-D to track AIGTs over time and observe different trends of
+AI Attribution Rate (AAR) across social media platforms from January 2022 to
+October 2024. Specifically, Medium and Quora exhibit marked increases in AAR,
+rising from 1.77% to 37.03% and 2.06% to 38.95%, respectively. In contrast,
+Reddit shows slower growth, with AAR increasing from 1.31% to 2.45% over the
+same period. Our further analysis indicates that AIGTs differ from
+human-written texts across several dimensions, including linguistic patterns,
+topic distributions, engagement levels, and the follower distribution of
+authors. We envision our analysis and findings on AIGTs in social media can
+shed light on future research in this domain.
+
+
+
+ comment: 24 pages,18 figures
+
+
+
+ + ☆ Ensuring Consistency for In-Image Translation +
+
+
+
+
+
+
+
+ Chengpeng Fu, Xiaocheng Feng, Yichong Huang, Wenshuai Huo, Baohang Li, Zhirui Zhang, Yunfei Lu, Dandan Tu, Duyu Tang, Hui Wang, Bing Qin, Ting Liu
+
+
+ The in-image machine translation task involves translating text embedded
+within images, with the translated results presented in image format. While
+this task has numerous applications in various scenarios such as film poster
+translation and everyday scene image translation, existing methods frequently
+neglect the aspect of consistency throughout this process. We propose the need
+to uphold two types of consistency in this task: translation consistency and
+image generation consistency. The former entails incorporating image
+information during translation, while the latter involves maintaining
+consistency between the style of the text-image and the original image,
+ensuring background integrity. To address these consistency requirements, we
+introduce a novel two-stage framework named HCIIT (High-Consistency In-Image
+Translation) which involves text-image translation using a multimodal
+multilingual large language model in the first stage and image backfilling with
+a diffusion model in the second stage. Chain of thought learning is utilized in
+the first stage to enhance the model's ability to leverage image information
+during translation. Subsequently, a diffusion model trained for
+style-consistent text-image generation ensures uniformity in text style within
+images and preserves background details. A dataset comprising 400,000
+style-consistent pseudo text-image pairs is curated for model training. Results
+obtained on both curated test sets and authentic image test sets validate the
+effectiveness of our framework in ensuring consistency and producing
+high-quality translated images.
+
+
+
+
+ + ☆ LSAQ: Layer-Specific Adaptive Quantization for Large Language Model + Deployment +
+
+
+
+
+
+
+
+ Binrui Zeng, Bin Ji, Xiaodong Liu, Jie Yu, Shasha Li, Jun Ma, Xiaopeng Li, Shangwen Wang, Xinran Hong
+
+
+ As large language models (LLMs) demonstrate exceptional performance across
+various domains, the deployment of these models on edge devices has emerged as
+a new trend. Quantization techniques, which reduce the size and memory
+footprint of LLMs, are effective for enabling deployment on
+resource-constrained edge devices. However, existing one-size-fits-all
+quantization methods often fail to dynamically adjust the memory consumption of
+LLMs based on specific hardware characteristics and usage scenarios. To address
+this limitation, we propose LSAQ (Layer-Specific Adaptive Quantization), a
+system for adaptive quantization and dynamic deployment of LLMs based on layer
+importance. LSAQ evaluates layer importance by constructing top-k token sets
+from the inputs and outputs of each layer and calculating their Jaccard
+coefficient. Using this evaluation, the system adaptively adjusts quantization
+strategies in real time according to the resource availability of edge devices,
+assigning different precision levels to layers of varying importance. This
+approach significantly reduces the storage requirements of LLMs while
+maintaining model performance, enabling efficient deployment across diverse
+hardware platforms and usage scenarios.
+
+
+
+ comment: 8 pages, 4 figures, work in progress
+
+
+
+ + ☆ AEIOU: A Unified Defense Framework against NSFW Prompts in Text-to-Image + Models +
+ +
+ As text-to-image (T2I) models continue to advance and gain widespread
+adoption, their associated safety issues are becoming increasingly prominent.
+Malicious users often exploit these models to generate Not-Safe-for-Work (NSFW)
+images using harmful or adversarial prompts, highlighting the critical need for
+robust safeguards to ensure the integrity and compliance of model outputs.
+Current internal safeguards frequently degrade image quality, while external
+detection methods often suffer from low accuracy and inefficiency.
+ In this paper, we introduce AEIOU, a defense framework that is Adaptable,
+Efficient, Interpretable, Optimizable, and Unified against NSFW prompts in T2I
+models. AEIOU extracts NSFW features from the hidden states of the model's text
+encoder, utilizing the separable nature of these features to detect NSFW
+prompts. The detection process is efficient, requiring minimal inference time.
+AEIOU also offers real-time interpretation of results and supports optimization
+through data augmentation techniques. The framework is versatile, accommodating
+various T2I architectures. Our extensive experiments show that AEIOU
+significantly outperforms both commercial and open-source moderation tools,
+achieving over 95% accuracy across all datasets and improving efficiency by at
+least tenfold. It effectively counters adaptive attacks and excels in few-shot
+and multi-label scenarios.
+
+
+
+
+ + ☆ Do Language Models Understand the Cognitive Tasks Given to Them? + Investigations with the N-Back Paradigm +
+ +
+ Cognitive tasks originally developed for humans are now increasingly used to
+study language models. While applying these tasks is often straightforward,
+interpreting their results can be challenging. In particular, when a model
+underperforms, it's often unclear whether this results from a limitation in the
+cognitive ability being tested or a failure to understand the task itself. A
+recent study argued that GPT 3.5's declining performance on 2-back and 3-back
+tasks reflects a working memory capacity limit similar to humans. By analyzing
+a range of open-source language models of varying performance levels on these
+tasks, we show that the poor performance instead reflects a limitation in task
+comprehension and task set maintenance. In addition, we push the best
+performing model to higher n values and experiment with alternative prompting
+strategies, before analyzing model attentions. Our larger aim is to contribute
+to the ongoing conversation around refining methodologies for the cognitive
+evaluation of language models.
+
+
+
+
+ + ☆ Molly: Making Large Language Model Agents Solve Python Problem More + Logically +
+ +
+ Applying large language models (LLMs) as teaching assists has attracted much
+attention as an integral part of intelligent education, particularly in
+computing courses. To reduce the gap between the LLMs and the computer
+programming education expert, fine-tuning and retrieval augmented generation
+(RAG) are the two mainstream methods in existing researches. However,
+fine-tuning for specific tasks is resource-intensive and may diminish the
+model`s generalization capabilities. RAG can perform well on reducing the
+illusion of LLMs, but the generation of irrelevant factual content during
+reasoning can cause significant confusion for learners. To address these
+problems, we introduce the Molly agent, focusing on solving the proposed
+problem encountered by learners when learning Python programming language. Our
+agent automatically parse the learners' questioning intent through a
+scenario-based interaction, enabling precise retrieval of relevant documents
+from the constructed knowledge base. At generation stage, the agent reflect on
+the generated responses to ensure that they not only align with factual content
+but also effectively answer the user's queries. Extensive experimentation on a
+constructed Chinese Python QA dataset shows the effectiveness of the Molly
+agent, indicating an enhancement in its performance for providing useful
+responses to Python questions.
+
+
+
+ comment: arXiv admin note: text overlap with arXiv:2402.07913
+
+
+
+ + ☆ Generating Traffic Scenarios via In-Context Learning to Learn Better + Motion Planner +
+ +
+ Motion planning is a crucial component in autonomous driving.
+State-of-the-art motion planners are trained on meticulously curated datasets,
+which are not only expensive to annotate but also insufficient in capturing
+rarely seen critical scenarios. Failing to account for such scenarios poses a
+significant risk to motion planners and may lead to incidents during testing.
+An intuitive solution is to manually compose such scenarios by programming and
+executing a simulator (e.g., CARLA). However, this approach incurs substantial
+human costs. Motivated by this, we propose an inexpensive method for generating
+diverse critical traffic scenarios to train more robust motion planners. First,
+we represent traffic scenarios as scripts, which are then used by the simulator
+to generate traffic scenarios. Next, we develop a method that accepts
+user-specified text descriptions, which a Large Language Model (LLM) translates
+into scripts using in-context learning. The output scripts are sent to the
+simulator that produces the corresponding traffic scenarios. As our method can
+generate abundant safety-critical traffic scenarios, we use them as synthetic
+training data for motion planners. To demonstrate the value of generated
+scenarios, we train existing motion planners on our synthetic data, real-world
+datasets, and a combination of both. Our experiments show that motion planners
+trained with our data significantly outperform those trained solely on
+real-world data, showing the usefulness of our synthetic data and the
+effectiveness of our data generation method. Our source code is available at
+https://ezharjan.github.io/AutoSceneGen.
+
+
+
+
+ + ☆ MMFactory: A Universal Solution Search Engine for Vision-Language Tasks +
+ +
+ With advances in foundational and vision-language models, and effective
+fine-tuning techniques, a large number of both general and special-purpose
+models have been developed for a variety of visual tasks. Despite the
+flexibility and accessibility of these models, no single model is able to
+handle all tasks and/or applications that may be envisioned by potential users.
+Recent approaches, such as visual programming and multimodal LLMs with
+integrated tools aim to tackle complex visual tasks, by way of program
+synthesis. However, such approaches overlook user constraints (e.g.,
+performance / computational needs), produce test-time sample-specific solutions
+that are difficult to deploy, and, sometimes, require low-level instructions
+that maybe beyond the abilities of a naive user. To address these limitations,
+we introduce MMFactory, a universal framework that includes model and metrics
+routing components, acting like a solution search engine across various
+available models. Based on a task description and few sample input-output pairs
+and (optionally) resource and/or performance constraints, MMFactory can suggest
+a diverse pool of programmatic solutions by instantiating and combining
+visio-lingual tools from its model repository. In addition to synthesizing
+these solutions, MMFactory also proposes metrics and benchmarks performance /
+resource characteristics, allowing users to pick a solution that meets their
+unique design constraints. From the technical perspective, we also introduced a
+committee-based solution proposer that leverages multi-agent LLM conversation
+to generate executable, diverse, universal, and robust solutions for the user.
+Experimental results show that MMFactory outperforms existing methods by
+delivering state-of-the-art solutions tailored to user problem specifications.
+Project page is available at https://davidhalladay.github.io/mmfactory_demo.
+
+
+
+
+ + ☆ Improving Factuality with Explicit Working Memory +
+
+
+
+
+
+
+
+ Mingda Chen, Yang Li, Karthik Padthe, Rulin Shao, Alicia Sun, Luke Zettlemoyer, Gargi Gosh, Wen-tau Yih
+
+
+ Large language models can generate factually inaccurate content, a problem
+known as hallucination. Recent works have built upon retrieved-augmented
+generation to improve factuality through iterative prompting but these methods
+are limited by the traditional RAG design. To address these challenges, we
+introduce EWE (Explicit Working Memory), a novel approach that enhances
+factuality in long-form text generation by integrating a working memory that
+receives real-time feedback from external resources. The memory is refreshed
+based on online fact-checking and retrieval feedback, allowing EWE to rectify
+false claims during the generation process and ensure more accurate and
+reliable outputs. Our experiments demonstrate that Ewe outperforms strong
+baselines on four fact-seeking long-form generation datasets, increasing the
+factuality metric, VeriScore, by 2 to 10 points absolute without sacrificing
+the helpfulness of the responses. Further analysis reveals that the design of
+rules for memory updates, configurations of memory units, and the quality of
+the retrieval datastore are crucial factors for influencing model performance.
+
+
+
+
+ + ☆ Lla-VAP: LSTM Ensemble of Llama and VAP for Turn-Taking Prediction +
+ +
+ Turn-taking prediction is the task of anticipating when the speaker in a
+conversation will yield their turn to another speaker to begin speaking. This
+project expands on existing strategies for turn-taking prediction by employing
+a multi-modal ensemble approach that integrates large language models (LLMs)
+and voice activity projection (VAP) models. By combining the linguistic
+capabilities of LLMs with the temporal precision of VAP models, we aim to
+improve the accuracy and efficiency of identifying TRPs in both scripted and
+unscripted conversational scenarios. Our methods are evaluated on the
+In-Conversation Corpus (ICC) and Coached Conversational Preference Elicitation
+(CCPE) datasets, highlighting the strengths and limitations of current models
+while proposing a potentially more robust framework for enhanced prediction.
+
+
+
+
+ + ☆ Neuron Empirical Gradient: Connecting Neurons' Linear Controllability + and Representational Capacity +
+ +
+ Although neurons in the feed-forward layers of pre-trained language models
+(PLMs) can store factual knowledge, most prior analyses remain qualitative,
+leaving the quantitative relationship among knowledge representation, neuron
+activations, and model output poorly understood. In this study, by performing
+neuron-wise interventions using factual probing datasets, we first reveal the
+linear relationship between neuron activations and output token probabilities.
+We refer to the gradient of this linear relationship as ``neuron empirical
+gradients.'' and propose NeurGrad, an efficient method for their calculation to
+facilitate quantitative neuron analysis. We next investigate whether neuron
+empirical gradients in PLMs encode general task knowledge by probing skill
+neurons. To this end, we introduce MCEval8k, a multi-choice knowledge
+evaluation benchmark spanning six genres and 22 tasks. Our experiments confirm
+that neuron empirical gradients effectively capture knowledge, while skill
+neurons exhibit efficiency, generality, inclusivity, and interdependency. These
+findings link knowledge to PLM outputs via neuron empirical gradients, shedding
+light on how PLMs store knowledge. The code and dataset are released.
+
+
+
+ comment: 29 pages, 18 figures
+
+
+
+ + ♻ ☆ Tokens, the oft-overlooked appetizer: Large language models, the + distributional hypothesis, and meaning +
+
+
+
+
+
+
+
+ Julia Witte Zimmerman, Denis Hudon, Kathryn Cramer, Alejandro J. Ruiz, Calla Beauregard, Ashley Fehr, Mikaela Irene Fudolig, Bradford Demarest, Yoshi Meke Bird, Milo Z. Trujillo, Christopher M. Danforth, Peter Sheridan Dodds
+
+
+ Tokenization is a necessary component within the current architecture of many
+language models, including the transformer-based large language models (LLMs)
+of Generative AI, yet its impact on the model's cognition is often overlooked.
+We argue that LLMs demonstrate that the Distributional Hypothesis (DH) is
+sufficient for reasonably human-like language performance, and that the
+emergence of human-meaningful linguistic units among tokens motivates
+linguistically-informed interventions in existing, linguistically-agnostic
+tokenization techniques, particularly with respect to their roles as (1)
+semantic primitives and as (2) vehicles for conveying salient distributional
+patterns from human language to the model. We explore tokenizations from a BPE
+tokenizer; extant model vocabularies obtained from Hugging Face and tiktoken;
+and the information in exemplar token vectors as they move through the layers
+of a RoBERTa (large) model. Besides creating sub-optimal semantic building
+blocks and obscuring the model's access to the necessary distributional
+patterns, we describe how tokenization pretraining can be a backdoor for bias
+and other unwanted content, which current alignment practices may not
+remediate. Additionally, we relay evidence that the tokenization algorithm's
+objective function impacts the LLM's cognition, despite being meaningfully
+insulated from the main system intelligence.
+
+
+
+
+ + ♻ ☆ Can LLMs Obfuscate Code? A Systematic Analysis of Large Language Models + into Assembly Code Obfuscation AAAI 2025 +
+
+
+
+
+
+
+
+ Seyedreza Mohseni, Seyedali Mohammadi, Deepa Tilwani, Yash Saxena, Gerald Ndawula, Sriram Vema, Edward Raff, Manas Gaur
+
+
+ Malware authors often employ code obfuscations to make their malware harder
+to detect. Existing tools for generating obfuscated code often require access
+to the original source code (e.g., C++ or Java), and adding new obfuscations is
+a non-trivial, labor-intensive process. In this study, we ask the following
+question: Can Large Language Models (LLMs) potentially generate a new
+obfuscated assembly code? If so, this poses a risk to anti-virus engines and
+potentially increases the flexibility of attackers to create new obfuscation
+patterns. We answer this in the affirmative by developing the MetamorphASM
+benchmark comprising MetamorphASM Dataset (MAD) along with three code
+obfuscation techniques: dead code, register substitution, and control flow
+change. The MetamorphASM systematically evaluates the ability of LLMs to
+generate and analyze obfuscated code using MAD, which contains 328,200
+obfuscated assembly code samples. We release this dataset and analyze the
+success rate of various LLMs (e.g., GPT-3.5/4, GPT-4o-mini, Starcoder,
+CodeGemma, CodeLlama, CodeT5, and LLaMA 3.1) in generating obfuscated assembly
+code. The evaluation was performed using established information-theoretic
+metrics and manual human review to ensure correctness and provide the
+foundation for researchers to study and develop remediations to this risk. The
+source code can be found at the following GitHub link:
+https://github.com/mohammadi-ali/MetamorphASM.
+
+
+
+ comment: To appear in AAAI 2025, Main Track
+
+
+
+ + ♻ ☆ Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with + Large Language Models +
+ +
+ Aspect-based sentiment analysis (ABSA), a sequence labeling task, has
+attracted increasing attention in multilingual contexts. While previous
+research has focused largely on fine-tuning or training models specifically for
+ABSA, we evaluate large language models (LLMs) under zero-shot conditions to
+explore their potential to tackle this challenge with minimal task-specific
+adaptation. We conduct a comprehensive empirical evaluation of a series of LLMs
+on multilingual ABSA tasks, investigating various prompting strategies,
+including vanilla zero-shot, chain-of-thought (CoT), self-improvement,
+self-debate, and self-consistency, across nine different models. Results
+indicate that while LLMs show promise in handling multilingual ABSA, they
+generally fall short of fine-tuned, task-specific models. Notably, simpler
+zero-shot prompts often outperform more complex strategies, especially in
+high-resource languages like English. These findings underscore the need for
+further refinement of LLM-based approaches to effectively address ABSA task
+across diverse languages.
+
+
+
+
+ + ♻ ☆ SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking + State Space Models +
+
+
+
+
+
+
+
+ Shuaijie Shen, Chao Wang, Renzhuo Huang, Yan Zhong, Qinghai Guo, Zhichao Lu, Jianguo Zhang, Luziwei Leng
+
+
+ Known as low energy consumption networks, spiking neural networks (SNNs) have
+gained a lot of attention within the past decades. While SNNs are increasing
+competitive with artificial neural networks (ANNs) for vision tasks, they are
+rarely used for long sequence tasks, despite their intrinsic temporal dynamics.
+In this work, we develop spiking state space models (SpikingSSMs) for long
+sequence learning by leveraging on the sequence learning abilities of state
+space models (SSMs). Inspired by dendritic neuron structure, we hierarchically
+integrate neuronal dynamics with the original SSM block, meanwhile realizing
+sparse synaptic computation. Furthermore, to solve the conflict of event-driven
+neuronal dynamics with parallel computing, we propose a light-weight surrogate
+dynamic network which accurately predicts the after-reset membrane potential
+and compatible to learnable thresholds, enabling orders of acceleration in
+training speed compared with conventional iterative methods. On the long range
+arena benchmark task, SpikingSSM achieves competitive performance to
+state-of-the-art SSMs meanwhile realizing on average 90\% of network sparsity.
+On language modeling, our network significantly surpasses existing spiking
+large language models (spikingLLMs) on the WikiText-103 dataset with only a
+third of the model size, demonstrating its potential as backbone architecture
+for low computation cost LLMs.
+
+
+
+
+ + ♻ ☆ YuLan-Mini: An Open Data-efficient Language Model +
+
+
+
+
+
+
+
+ Yiwen Hu, Huatong Song, Jia Deng, Jiapeng Wang, Jie Chen, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Wayne Xin Zhao, Ji-Rong Wen
+
+
+ Effective pre-training of large language models (LLMs) has been challenging
+due to the immense resource demands and the complexity of the technical
+processes involved. This paper presents a detailed technical report on
+YuLan-Mini, a highly capable base model with 2.42B parameters that achieves
+top-tier performance among models of similar parameter scale. Our pre-training
+approach focuses on enhancing training efficacy through three key technical
+contributions: an elaborate data pipeline combines data cleaning with data
+schedule strategies, a robust optimization method to mitigate training
+instability, and an effective annealing approach that incorporates targeted
+data selection and long context training. Remarkably, YuLan-Mini, trained on
+1.08T tokens, achieves performance comparable to industry-leading models that
+require significantly more data. To facilitate reproduction, we release the
+full details of the data composition for each training phase. Project details
+can be accessed at the following link: https://github.com/RUC-GSAI/YuLan-Mini.
+
+
+
+
+ + ♻ ☆ BEADs: Bias Evaluation Across Domains +
+ +
+ Recent advancements in large language models (LLMs) have greatly enhanced
+natural language processing (NLP) applications. Nevertheless, these models
+often inherit biases from their training data. Despite the availability of
+various datasets for bias detection, most are limited to one or two NLP tasks
+(typically classification or evaluation) and lack comprehensive evaluations
+across a broader range of NLP tasks. To address this gap, we introduce the Bias
+Evaluations Across Domains BEADs dataset, designed to support a wide array of
+NLP tasks, including text classification, token classification, bias
+quantification, and benign language generation. A key focus of this paper is
+the gold label dataset that is annotated by GPT4 for scalabilty and verified by
+experts to ensure high reliability. BEADs provides data for both fine-tuning,
+including classification and language generation tasks, and for evaluating
+LLMs. Our findings indicate that BEADs effectively identifies numerous biases
+when fine-tuned on this dataset. It also reduces biases when used for
+fine-tuning language generation task, while preserving language quality. The
+results also reveal some prevalent demographic biases in LLMs when BEADs is
+used for evaluation in demographic task. We provide the BEADs dataset for
+detecting biases in various domains, and this dataset is readily usable for
+responsible AI development and application. The dataset can be accessed at
+https://huggingface.co/datasets/shainar/BEAD .
+
+
+
+ comment: under review
+
+
+
+ + ♻ ☆ Security Attacks on LLM-based Code Completion Tools AAAI 2025 +
+ +
+ The rapid development of large language models (LLMs) has significantly
+advanced code completion capabilities, giving rise to a new generation of
+LLM-based Code Completion Tools (LCCTs). Unlike general-purpose LLMs, these
+tools possess unique workflows, integrating multiple information sources as
+input and prioritizing code suggestions over natural language interaction,
+which introduces distinct security challenges. Additionally, LCCTs often rely
+on proprietary code datasets for training, raising concerns about the potential
+exposure of sensitive data. This paper exploits these distinct characteristics
+of LCCTs to develop targeted attack methodologies on two critical security
+risks: jailbreaking and training data extraction attacks. Our experimental
+results expose significant vulnerabilities within LCCTs, including a 99.4%
+success rate in jailbreaking attacks on GitHub Copilot and a 46.3% success rate
+on Amazon Q. Furthermore, We successfully extracted sensitive user data from
+GitHub Copilot, including 54 real email addresses and 314 physical addresses
+associated with GitHub usernames. Our study also demonstrates that these
+code-based attack methods are effective against general-purpose LLMs, such as
+the GPT series, highlighting a broader security misalignment in the handling of
+code by modern LLMs. These findings underscore critical security challenges
+associated with LCCTs and suggest essential directions for strengthening their
+security frameworks. The example code and attack samples from our research are
+provided at https://github.com/Sensente/Security-Attacks-on-LCCTs.
+
+
+
+ comment: Paper accepted at AAAI 2025
+
+
+
+ + ♻ ☆ SafeAligner: Safety Alignment against Jailbreak Attacks via Response + Disparity Guidance +
+
+
+
+
+
+
+
+ Caishuang Huang, Wanxu Zhao, Rui Zheng, Huijie Lv, Wenyu Zhan, Shihan Dou, Sixian Li, Xiao Wang, Enyu Zhou, Junjie Ye, Yuming Yang, Tao Gui, Qi Zhang, Xuanjing Huang
+
+
+ As the development of large language models (LLMs) rapidly advances, securing
+these models effectively without compromising their utility has become a
+pivotal area of research. However, current defense strategies against jailbreak
+attacks (i.e., efforts to bypass security protocols) often suffer from limited
+adaptability, restricted general capability, and high cost. To address these
+challenges, we introduce SafeAligner, a methodology implemented at the decoding
+stage to fortify defenses against jailbreak attacks. We begin by developing two
+specialized models: the Sentinel Model, which is trained to foster safety, and
+the Intruder Model, designed to generate riskier responses. SafeAligner
+leverages the disparity in security levels between the responses from these
+models to differentiate between harmful and beneficial tokens, effectively
+guiding the safety alignment by altering the output token distribution of the
+target model. Extensive experiments show that SafeAligner can increase the
+likelihood of beneficial tokens, while reducing the occurrence of harmful ones,
+thereby ensuring secure alignment with minimal loss to generality.
+
+
+
+
+ + ♻ ☆ Prompted Contextual Vectors for Spear-Phishing Detection +
+ +
+ Spear-phishing attacks present a significant security challenge, with large
+language models (LLMs) escalating the threat by generating convincing emails
+and facilitating target reconnaissance. To address this, we propose a detection
+approach based on a novel document vectorization method that utilizes an
+ensemble of LLMs to create representation vectors. By prompting LLMs to reason
+and respond to human-crafted questions, we quantify the presence of common
+persuasion principles in the email's content, producing prompted contextual
+document vectors for a downstream supervised machine learning model. We
+evaluate our method using a unique dataset generated by a proprietary system
+that automates target reconnaissance and spear-phishing email creation. Our
+method achieves a 91\% F1 score in identifying LLM-generated spear-phishing
+emails, with the training set comprising only traditional phishing and benign
+emails. Key contributions include a novel document vectorization method
+utilizing LLM reasoning, a publicly available dataset of high-quality
+spear-phishing emails, and the demonstrated effectiveness of our method in
+detecting such emails. This methodology can be utilized for various document
+classification tasks, particularly in adversarial problem domains.
+
+
+
+
+ + ♻ ☆ A Review of the Marathi Natural Language Processing +
+ +
+ Marathi is one of the most widely used languages in the world. One might
+expect that the latest advances in NLP research in languages like English reach
+such a large community. However, NLP advancements in English didn't immediately
+reach Indian languages like Marathi. There were several reasons for this. They
+included diversity of scripts used, lack of (publicly available) resources like
+tokenization strategies, high quality datasets \& benchmarks, and evaluation
+metrics. In addition to this, the morphologically rich nature of Marathi, made
+NLP tasks challenging. Advances in Neural Network (NN) based models and tools
+since the early 2000s helped improve this situation and make NLP research more
+accessible. In the past 10 years, significant efforts were made to improve
+language resources for all 22 scheduled languages of India. This paper presents
+a broad overview of evolution of NLP research in Indic languages with a focus
+on Marathi and state-of-the-art resources and tools available to the research
+community. It also provides an overview of tools \& techniques associated with
+Marathi NLP tasks.
+
+
+
+
+ + ♻ ☆ UniHR: Hierarchical Representation Learning for Unified Knowledge Graph + Link Prediction +
+
+
+
+
+
+
+
+ Zhiqiang Liu, Mingyang Chen, Yin Hua, Zhuo Chen, Ziqi Liu, Lei Liang, Huajun Chen, Wen Zhang
+
+
+ Beyond-triple fact representations including hyper-relational facts with
+auxiliary key-value pairs, temporal facts with additional timestamps, and
+nested facts implying relationships between facts, are gaining significant
+attention. However, existing link prediction models are usually designed for
+one specific type of facts, making it difficult to generalize to other fact
+representations. To overcome this limitation, we propose a Unified Hierarchical
+Representation learning framework (UniHR) for unified knowledge graph link
+prediction. It consists of a unified Hierarchical Data Representation (HiDR)
+module and a unified Hierarchical Structure Learning (HiSL) module as graph
+encoder. The HiDR module unifies hyper-relational KGs, temporal KGs, and nested
+factual KGs into triple-based representations. Then HiSL incorporates
+intra-fact and inter-fact message passing, focusing on enhancing the semantic
+information within individual facts and enriching the structural information
+between facts. Experimental results across 7 datasets from 3 types of KGs
+demonstrate that our UniHR outperforms baselines designed for one specific kind
+of KG, indicating strong generalization capability of HiDR form and the
+effectiveness of HiSL module. Code and data are available at
+https://github.com/Lza12a/UniHR.
+
+
+
+
+ + ♻ ☆ TableRAG: Million-Token Table Understanding with Language Models NeurIPS 2024 +
+
+
+
+
+
+
+
+ Si-An Chen, Lesly Miculicich, Julian Martin Eisenschlos, Zifeng Wang, Zilong Wang, Yanfei Chen, Yasuhisa Fujii, Hsuan-Tien Lin, Chen-Yu Lee, Tomas Pfister
+
+
+ Recent advancements in language models (LMs) have notably enhanced their
+ability to reason with tabular data, primarily through program-aided mechanisms
+that manipulate and analyze tables. However, these methods often require the
+entire table as input, leading to scalability challenges due to the positional
+bias or context length constraints. In response to these challenges, we
+introduce TableRAG, a Retrieval-Augmented Generation (RAG) framework
+specifically designed for LM-based table understanding. TableRAG leverages
+query expansion combined with schema and cell retrieval to pinpoint crucial
+information before providing it to the LMs. This enables more efficient data
+encoding and precise retrieval, significantly reducing prompt lengths and
+mitigating information loss. We have developed two new million-token benchmarks
+from the Arcade and BIRD-SQL datasets to thoroughly evaluate TableRAG's
+effectiveness at scale. Our results demonstrate that TableRAG's retrieval
+design achieves the highest retrieval quality, leading to the new
+state-of-the-art performance on large-scale table understanding.
+
+
+
+ comment: Accepted to NeurIPS 2024
+
+
+
+ + ♻ ☆ HashEvict: A Pre-Attention KV Cache Eviction Strategy using + Locality-Sensitive Hashing +
+
+
+
+
+
+
+
+ Minghui Liu, Tahseen Rabbani, Tony O'Halloran, Ananth Sankaralingam, Mary-Anne Hartley, Brian Gravelle, Furong Huang, Cornelia Fermüller, Yiannis Aloimonos
+
+
+ Transformer-based large language models (LLMs) use the key-value (KV) cache
+to significantly accelerate inference by storing the key and value embeddings
+of past tokens. However, this cache consumes significant GPU memory. In this
+work, we introduce HashEvict, an algorithm that uses locality-sensitive hashing
+(LSH) to compress the KV cache. HashEvict quickly locates tokens in the cache
+that are cosine dissimilar to the current query token. This is achieved by
+computing the Hamming distance between binarized Gaussian projections of the
+current token query and cached token keys, with a projection length much
+smaller than the embedding dimension. We maintain a lightweight binary
+structure in GPU memory to facilitate these calculations. Unlike existing
+compression strategies that compute attention to determine token retention,
+HashEvict makes these decisions pre-attention, thereby reducing computational
+costs. Additionally, HashEvict is dynamic - at every decoding step, the key and
+value of the current token replace the embeddings of a token expected to
+produce the lowest attention score. We demonstrate that HashEvict can compress
+the KV cache by 30%-70% while maintaining high performance across reasoning,
+multiple-choice, long-context retrieval and summarization tasks.
+
+
+
+ comment: 10 pages, 6 figures, 2 tables
+
+
+
+ + ♻ ☆ System-2 Mathematical Reasoning via Enriched Instruction Tuning +
+ +
+ Solving complex mathematical problems via system-2 reasoning is a natural
+human skill, yet it remains a significant challenge for current large language
+models (LLMs). We identify the scarcity of deliberate multi-step reasoning data
+as a primary limiting factor. To this end, we introduce Enriched Instruction
+Tuning (EIT), a method that enriches existing human-annotated mathematical
+datasets by synergizing human and AI feedback to create fine-grained reasoning
+trajectories. These datasets are then used to fine-tune open-source LLMs,
+enhancing their mathematical reasoning abilities without reliance on any
+symbolic verification program. Concretely, EIT is composed of two critical
+steps: Enriching with Reasoning Plan (ERP) and Enriching with Reasoning Step
+(ERS). The former generates a high-level plan that breaks down complex
+instructions into a sequence of simpler objectives, while ERS fills in
+reasoning contexts often overlooked by human annotators, creating a smoother
+reasoning trajectory for LLM fine-tuning. Unlike existing CoT prompting methods
+that generate reasoning chains only depending on LLM's internal knowledge, our
+method leverages human-annotated initial answers as ``meta-knowledge'' to help
+LLMs generate more detailed and precise reasoning processes, leading to a more
+trustworthy LLM expert for complex mathematical problems. In experiments, EIT
+achieves an accuracy of 84.1% on GSM8K and 32.5% on MATH, surpassing
+state-of-the-art fine-tuning and prompting methods, and even matching the
+performance of tool-augmented methods.
+
+
+
+
+ + ♻ ☆ RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF + for Conversational QA over KGs with RAG +
+
+
+
+
+
+
+
+ Rishiraj Saha Roy, Chris Hinze, Joel Schlotthauer, Farzad Naderi, Viktor Hangya, Andreas Foltyn, Luzian Hahn, Fabian Kuech
+
+
+ Conversational question answering (ConvQA) is a convenient means of searching
+over RDF knowledge graphs (KGs), where a prevalent approach is to translate
+natural language questions to SPARQL queries. However, SPARQL has certain
+shortcomings: (i) it is brittle for complex intents and conversational
+questions, and (ii) it is not suitable for more abstract needs. Instead, we
+propose a novel two-pronged system where we fuse: (i) SQL-query results over a
+database automatically derived from the KG, and (ii) text-search results over
+verbalizations of KG facts. Our pipeline supports iterative retrieval: when the
+results of any branch are found to be unsatisfactory, the system can
+automatically opt for further rounds. We put everything together in a retrieval
+augmented generation (RAG) setup, where an LLM generates a coherent response
+from accumulated search results. We demonstrate the superiority of our proposed
+system over several baselines on a knowledge graph of BMW automobiles.
+
+
+
+ comment: Accepted at BTW 2025, 10 pages
+
+
+
+ + ♻ ☆ Exploring Facets of Language Generation in the Limit +
+ +
+ The recent work of Kleinberg & Mullainathan [KM24] provides a concrete model
+for language generation in the limit: given a sequence of examples from an
+unknown target language, the goal is to generate new examples from the target
+language such that no incorrect examples are generated beyond some point. In
+sharp contrast to strong negative results for the closely related problem of
+language identification, they establish positive results for language
+generation in the limit for all countable collections of languages. Follow-up
+work by Raman & Tewari [RT24] studies bounds on the number of distinct inputs
+required by an algorithm before correct language generation is achieved --
+namely, whether this is a constant for all languages in the collection (uniform
+generation) or a language-dependent constant (non-uniform generation).
+ We show that every countable language collection has a generator which has
+the stronger property of non-uniform generation in the limit. However, while
+the generation algorithm of [KM24] can be implemented using membership queries,
+we show that any algorithm cannot non-uniformly generate even for collections
+of just two languages, using only membership queries.
+ We also formalize the tension between validity and breadth in the generation
+algorithm of [KM24] by introducing a definition of exhaustive generation, and
+show a strong negative result for exhaustive generation. Our result shows that
+a tradeoff between validity and breadth is inherent for generation in the
+limit. We also provide a precise characterization of the language collections
+for which exhaustive generation is possible. Finally, inspired by algorithms
+that can choose to obtain feedback, we consider a model of uniform generation
+with feedback, completely characterizing language collections for which such
+uniform generation with feedback is possible in terms of a complexity measure
+of the collection.
+
+
+
+ comment: 31 pages. Fixed typos, updated related work, added results on
+ characterization of exhaustive generation
+
+
+
+ + ♻ ☆ XRAG: eXamining the Core -- Benchmarking Foundational Components in + Advanced Retrieval-Augmented Generation +
+
+
+
+
+
+
+
+ Qianren Mao, Yangyifei Luo, Jinlong Zhang, Hanwen Hao, Zhilong Cao, Xiaolong Wang, Xiao Guan, Zhenting Huang, Weifeng Jiang, Shuyu Guo, Zhentao Han, Qili Zhang, Siyuan Tao, Yujie Liu, Junnan Liu, Zhixing Tan, Jie Sun, Bo Li, Xudong Liu, Richong Zhang, Jianxin Li
+
+
+ Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent
+data with the generative capabilities of Large Language Models (LLMs), ensuring
+that the generated output is not only contextually relevant but also accurate
+and current. We introduce XRAG, an open-source, modular codebase that
+facilitates exhaustive evaluation of the performance of foundational components
+of advanced RAG modules. These components are systematically categorized into
+four core phases: pre-retrieval, retrieval, post-retrieval, and generation. We
+systematically analyse them across reconfigured datasets, providing a
+comprehensive benchmark for their effectiveness. As the complexity of RAG
+systems continues to escalate, we underscore the critical need to identify
+potential failure points in RAG systems. We formulate a suite of experimental
+methodologies and diagnostic testing protocols to dissect the failure points
+inherent in RAG engineering. Subsequently, we proffer bespoke solutions aimed
+at bolstering the overall performance of these modules. Our work thoroughly
+evaluates the performance of advanced core components in RAG systems, providing
+insights into optimizations for prevalent failure points.
+
+
+
+
+ + ♻ ☆ Listening to Patients: A Framework of Detecting and Mitigating Patient + Misreport for Medical Dialogue Generation +
+ +
+ Medical Dialogue Systems aim to provide automated healthcare support through
+patient-agent conversations. Previous efforts typically regard patients as
+ideal users -- one who accurately and consistently reports their health
+conditions. However, in reality, patients often misreport their symptoms,
+leading to discrepancies between their reports and actual health conditions.
+Overlooking patient misreport will affect the quality of healthcare
+consultations provided by MDS. To address this issue, we argue that MDS should
+''listen to patients'' and tackle two key challenges: how to detect and
+mitigate patient misreport effectively. In this work, we propose PaMis, a
+framework of detecting and mitigating Patient Misreport for medical dialogue
+generation. PaMis first constructs dialogue entity graphs, then detects patient
+misreport based on graph entropy, and mitigates patient misreport by
+formulating clarifying questions. Experiments indicate that PaMis effectively
+enhances medical response generation, enabling models like GPT-4 to detect and
+mitigate patient misreports, and provide high-quality healthcare assistance.
+
+
+
+
+ + ♻ ☆ Re-examining learning linear functions in context +
+ +
+ In-context learning (ICL) has emerged as a powerful paradigm for easily
+adapting Large Language Models (LLMs) to various tasks. However, our
+understanding of how ICL works remains limited. We explore a simple model of
+ICL in a controlled setup with synthetic training data to investigate ICL of
+univariate linear functions. We experiment with a range of GPT-2-like
+transformer models trained from scratch. Our findings challenge the prevailing
+narrative that transformers adopt algorithmic approaches like linear regression
+to learn a linear function in-context. These models fail to generalize beyond
+their training distribution, highlighting fundamental limitations in their
+capacity to infer abstract task structures. Our experiments lead us to propose
+a mathematically precise hypothesis of what the model might be learning.
+
+
+
+
+ + ♻ ☆ GPTEval: A Survey on Assessments of ChatGPT and GPT-4 +
+ +
+ The emergence of ChatGPT has generated much speculation in the press about
+its potential to disrupt social and economic systems. Its astonishing language
+ability has aroused strong curiosity among scholars about its performance in
+different domains. There have been many studies evaluating the ability of
+ChatGPT and GPT-4 in different tasks and disciplines. However, a comprehensive
+review summarizing the collective assessment findings is lacking. The objective
+of this survey is to thoroughly analyze prior assessments of ChatGPT and GPT-4,
+focusing on its language and reasoning abilities, scientific knowledge, and
+ethical considerations. Furthermore, an examination of the existing evaluation
+methods is conducted, offering several recommendations for future research in
+evaluating large language models.
+
+
+
+
+ + ♻ ☆ Open-Vocabulary Mobile Manipulation Based on Double Relaxed Contrastive + Learning with Dense Labeling +
+ +
+ Growing labor shortages are increasing the demand for domestic service robots
+(DSRs) to assist in various settings. In this study, we develop a DSR that
+transports everyday objects to specified pieces of furniture based on
+open-vocabulary instructions. Our approach focuses on retrieving images of
+target objects and receptacles from pre-collected images of indoor
+environments. For example, given an instruction "Please get the right red towel
+hanging on the metal towel rack and put it in the white washing machine on the
+left," the DSR is expected to carry the red towel to the washing machine based
+on the retrieved images. This is challenging because the correct images should
+be retrieved from thousands of collected images, which may include many images
+of similar towels and appliances. To address this, we propose RelaX-Former,
+which learns diverse and robust representations from among positive, unlabeled
+positive, and negative samples. We evaluated RelaX-Former on a dataset
+containing real-world indoor images and human annotated instructions including
+complex referring expressions. The experimental results demonstrate that
+RelaX-Former outperformed existing baseline models across standard image
+retrieval metrics. Moreover, we performed physical experiments using a DSR to
+evaluate the performance of our approach in a zero-shot transfer setting. The
+experiments involved the DSR to carry objects to specific receptacles based on
+open-vocabulary instructions, achieving an overall success rate of 75%.
+
+
+
+ comment: Accepted for IEEE RA-L 2025
+
+
+
+ + ♻ ☆ On the loss of context-awareness in general instruction fine-tuning +
+ +
+ Pre-trained Large Language Models (LLMs) require post-training methods such
+as supervised fine-tuning (SFT) on instruction-response pairs to enable
+instruction following. However, this process can potentially harm existing
+capabilities learned during pre-training. In this paper, we investigate the
+loss of context awareness after SFT, where context awareness is defined as the
+ability to extract and understand information from user-provided context and
+respond accordingly. We are the first to identify and show that the loss of
+context awareness, as reflected by the performance drop in the
+Needle-in-a-Haystack test, occurs in instruction fine-tuned LLMs when the chat
+template is applied to input prompts. We identify that the performance decline
+is partially caused by an attention bias toward different roles learned during
+conversational instruction fine-tuning. We validate our hypothesis by
+visualizing changes in attention allocation after the chat template is applied
+and manually steering the attention heads. Based on these observations, we
+propose a metric to select context-dependent examples from general instruction
+fine-tuning datasets. We then apply conditional instruction fine-tuning with a
+context-dependency indicator, enabling the model to learn context awareness
+from these selected examples. Empirical experiments on four context-dependent
+downstream tasks and three pre-trained LLMs of different sizes show that our
+method effectively mitigates the loss of context awareness without compromising
+general instruction-following capabilities. Given our findings, we strongly
+advocate for careful benchmarking of context awareness after instruction
+fine-tuning.
+
+
+
+
+ + ♻ ☆ LLM-GAN: Construct Generative Adversarial Network Through Large Language + Models For Explainable Fake News Detection +
+
+
+
+
+
+
+
+ Yifeng Wang, Zhouhong Gu, Siwei Zhang, Suhang Zheng, Tao Wang, Tianyu Li, Hongwei Feng, Yanghua Xiao
+
+
+ Explainable fake news detection predicts the authenticity of news items with
+annotated explanations. Today, Large Language Models (LLMs) are known for their
+powerful natural language understanding and explanation generation abilities.
+However, presenting LLMs for explainable fake news detection remains two main
+challenges. Firstly, fake news appears reasonable and could easily mislead
+LLMs, leaving them unable to understand the complex news-faking process.
+Secondly, utilizing LLMs for this task would generate both correct and
+incorrect explanations, which necessitates abundant labor in the loop. In this
+paper, we propose LLM-GAN, a novel framework that utilizes prompting mechanisms
+to enable an LLM to become Generator and Detector and for realistic fake news
+generation and detection. Our results demonstrate LLM-GAN's effectiveness in
+both prediction performance and explanation quality. We further showcase the
+integration of LLM-GAN to a cloud-native AI platform to provide better fake
+news detection service in the cloud.
+
+
+
+
+ + ♻ ☆ Improvement in Sign Language Translation Using Text CTC Alignment +
+ +
+ Current sign language translation (SLT) approaches often rely on gloss-based
+supervision with Connectionist Temporal Classification (CTC), limiting their
+ability to handle non-monotonic alignments between sign language video and
+spoken text. In this work, we propose a novel method combining joint
+CTC/Attention and transfer learning. The joint CTC/Attention introduces
+hierarchical encoding and integrates CTC with the attention mechanism during
+decoding, effectively managing both monotonic and non-monotonic alignments.
+Meanwhile, transfer learning helps bridge the modality gap between vision and
+language in SLT. Experimental results on two widely adopted benchmarks,
+RWTH-PHOENIX-Weather 2014 T and CSL-Daily, show that our method achieves
+results comparable to state-of-the-art and outperforms the pure-attention
+baseline. Additionally, this work opens a new door for future research into
+gloss-free SLT using text-based CTC alignment.
+
+
+
+
+ + ♻ ☆ Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic + Knowledge Graph +
+ +
+ The rapid growth of social media platforms has raised significant concerns
+regarding online content toxicity. When Large Language Models (LLMs) are used
+for toxicity detection, two key challenges emerge: 1) the absence of
+domain-specific toxic knowledge leads to false negatives; 2) the excessive
+sensitivity of LLMs to toxic speech results in false positives, limiting
+freedom of speech. To address these issues, we propose a novel method called
+MetaTox, leveraging graph search on a meta-toxic knowledge graph to enhance
+hatred and toxicity detection. First, we construct a comprehensive meta-toxic
+knowledge graph by utilizing LLMs to extract toxic information through a
+three-step pipeline, with toxic benchmark datasets serving as corpora. Second,
+we query the graph via retrieval and ranking processes to supplement accurate,
+relevant toxic knowledge. Extensive experiments and in-depth case studies
+across multiple datasets demonstrate that our MetaTox significantly decreases
+the false positive rate while boosting overall toxicity detection performance.
+Our code will be available soon.
+
+
+
+ comment: 8 pages of content
+
+
+
+ + ♻ ☆ L3TC: Leveraging RWKV for Learned Lossless Low-Complexity Text + Compression +
+ +
+ Learning-based probabilistic models can be combined with an entropy coder for
+data compression. However, due to the high complexity of learning-based models,
+their practical application as text compressors has been largely overlooked. To
+address this issue, our work focuses on a low-complexity design while
+maintaining compression performance. We introduce a novel Learned Lossless
+Low-complexity Text Compression method (L3TC). Specifically, we conduct
+extensive experiments demonstrating that RWKV models achieve the fastest
+decoding speed with a moderate compression ratio, making it the most suitable
+backbone for our method. Second, we propose an outlier-aware tokenizer that
+uses a limited vocabulary to cover frequent tokens while allowing outliers to
+bypass the prediction and encoding. Third, we propose a novel high-rank
+reparameterization strategy that enhances the learning capability during
+training without increasing complexity during inference. Experimental results
+validate that our method achieves 48% bit saving compared to gzip compressor.
+Besides, L3TC offers compression performance comparable to other learned
+compressors, with a 50x reduction in model parameters. More importantly, L3TC
+is the fastest among all learned compressors, providing real-time decoding
+speeds up to megabytes per second. Our code is available at
+https://github.com/alipay/L3TC-leveraging-rwkv-for-learned-lossless-low-complexity-text-compression.git.
+
+
+
+
+ + ♻ ☆ Adapting Whisper for Code-Switching through Encoding Refining and + Language-Aware Decoding +
+
+
+
+
+
+
+
+ Jiahui Zhao, Hao Shi, Chenrui Cui, Tianrui Wang, Hexin Liu, Zhaoheng Ni, Lingxuan Ye, Longbiao Wang
+
+
+ Code-switching (CS) automatic speech recognition (ASR) faces challenges due
+to the language confusion resulting from accents, auditory similarity, and
+seamless language switches. Adaptation on the pre-trained multi-lingual model
+has shown promising performance for CS-ASR. In this paper, we adapt Whisper,
+which is a large-scale multilingual pre-trained speech recognition model, to CS
+from both encoder and decoder parts. First, we propose an encoder refiner to
+enhance the encoder's capacity of intra-sentence swithching. Second, we propose
+using two sets of language-aware adapters with different language prompt
+embeddings to achieve language-specific decoding information in each decoder
+layer. Then, a fusion module is added to fuse the language-aware decoding. The
+experimental results using the SEAME dataset show that, compared with the
+baseline model, the proposed approach achieves a relative MER reduction of 4.1%
+and 7.2% on the dev_man and dev_sge test sets, respectively, surpassing
+state-of-the-art methods. Through experiments, we found that the proposed
+method significantly improves the performance on non-native language in CS
+speech, indicating that our approach enables Whisper to better distinguish
+between the two languages.
+
+
+
+
+ + ♻ ☆ Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large + Language Models +
+
+
+
+
+
+
+
+ Bofei Gao, Feifan Song, Zhe Yang, Zefan Cai, Yibo Miao, Qingxiu Dong, Lei Li, Chenghao Ma, Liang Chen, Runxin Xu, Zhengyang Tang, Benyou Wang, Daoguang Zan, Shanghaoran Quan, Ge Zhang, Lei Sha, Yichang Zhang, Xuancheng Ren, Tianyu Liu, Baobao Chang
+
+
+ Recent advancements in large language models (LLMs) have led to significant
+breakthroughs in mathematical reasoning capabilities. However, existing
+benchmarks like GSM8K or MATH are now being solved with high accuracy (e.g.,
+OpenAI o1 achieves 94.8\% on MATH dataset), indicating their inadequacy for
+truly challenging these models. To bridge this gap, we propose a comprehensive
+and challenging benchmark specifically designed to assess LLMs' mathematical
+reasoning at the Olympiad level. Unlike existing Olympiad-related benchmarks,
+our dataset focuses exclusively on mathematics and comprises a vast collection
+of 4428 competition-level problems with rigorous human annotation. These
+problems are meticulously categorized into over 33 sub-domains and span more
+than 10 distinct difficulty levels, enabling a holistic assessment of model
+performance in Olympiad-mathematical reasoning. Furthermore, we conducted an
+in-depth analysis based on this benchmark. Our experimental results show that
+even the most advanced models, OpenAI o1-mini and OpenAI o1-preview, struggle
+with highly challenging Olympiad-level problems, with 60.54\% and 52.55\%
+accuracy, highlighting significant challenges in Olympiad-level mathematical
+reasoning.
+
+
+
+ comment: 30 pages
+
+
+
+ + ♻ ☆ Revisiting Jailbreaking for Large Language Models: A Representation + Engineering Perspective COLING 2025 +
+
+
+
+
+
+
+
+ Tianlong Li, Zhenghua Wang, Wenhao Liu, Muling Wu, Shihan Dou, Changze Lv, Xiaohua Wang, Xiaoqing Zheng, Xuanjing Huang
+
+
+ The recent surge in jailbreaking attacks has revealed significant
+vulnerabilities in Large Language Models (LLMs) when exposed to malicious
+inputs. While various defense strategies have been proposed to mitigate these
+threats, there has been limited research into the underlying mechanisms that
+make LLMs vulnerable to such attacks. In this study, we suggest that the
+self-safeguarding capability of LLMs is linked to specific activity patterns
+within their representation space. Although these patterns have little impact
+on the semantic content of the generated text, they play a crucial role in
+shaping LLM behavior under jailbreaking attacks. Our findings demonstrate that
+these patterns can be detected with just a few pairs of contrastive queries.
+Extensive experimentation shows that the robustness of LLMs against
+jailbreaking can be manipulated by weakening or strengthening these patterns.
+Further visual analysis provides additional evidence for our conclusions,
+providing new insights into the jailbreaking phenomenon. These findings
+highlight the importance of addressing the potential misuse of open-source LLMs
+within the community.
+
+
+
+ comment: Accepted by COLING 2025
+
+
+
+ + ♻ ☆ From Models to Microtheories: Distilling a Model's Topical Knowledge for + Grounded Question Answering +
+
+
+
+
+
+
+
+ Nathaniel Weir, Bhavana Dalvi Mishra, Orion Weller, Oyvind Tafjord, Sam Hornstein, Alexander Sabol, Peter Jansen, Benjamin Van Durme, Peter Clark
+
+
+ Recent reasoning methods (e.g., chain-of-thought, entailment reasoning) help
+users understand how language models (LMs) answer a single question, but they
+do little to reveal the LM's overall understanding, or "theory," about the
+question's topic, making it still hard to trust the model. Our goal is to
+materialize such theories - here called microtheories (a linguistic analog of
+logical microtheories) - as a set of sentences encapsulating an LM's core
+knowledge about a topic. These statements systematically work together to
+entail answers to a set of questions to both engender trust and improve
+performance. Our approach is to first populate a knowledge store with
+(model-generated) sentences that entail answers to training questions and then
+distill those down to a core microtheory that is concise, general, and
+non-redundant. We show that, when added to a general corpus (e.g., Wikipedia),
+microtheories can supply critical, topical information not necessarily present
+in the corpus, improving both a model's ability to ground its answers to
+verifiable knowledge (i.e., show how answers are systematically entailed by
+documents in the corpus, fully grounding up to +8% more answers), and the
+accuracy of those grounded answers (up to +8% absolute). We also show that, in
+a human evaluation in the medical domain, our distilled microtheories contain a
+significantly higher concentration of topically critical facts than the
+non-distilled knowledge store. Finally, we show we can quantify the coverage of
+a microtheory for a topic (characterized by a dataset) using a notion of
+$p$-relevance. Together, these suggest that microtheories are an efficient
+distillation of an LM's topic-relevant knowledge, that they can usefully
+augment existing corpora, and can provide both performance gains and an
+interpretable, verifiable window into the model's knowledge of a topic.
+
+
+
+
+ + ♻ ☆ Sim911: Towards Effective and Equitable 9-1-1 Dispatcher Training with + an LLM-Enabled Simulation +
+
+
+
+
+
+
+
+ Zirong Chen, Elizabeth Chason, Noah Mladenovski, Erin Wilson, Kristin Mullen, Stephen Martini, Meiyi Ma
+
+
+ Emergency response services are vital for enhancing public safety by
+safeguarding the environment, property, and human lives. As frontline members
+of these services, 9-1-1 dispatchers have a direct impact on response times and
+the overall effectiveness of emergency operations. However, traditional
+dispatcher training methods, which rely on role-playing by experienced
+personnel, are labor-intensive, time-consuming, and often neglect the specific
+needs of underserved communities. To address these challenges, we introduce
+Sim911, the first training simulation for 9-1-1 dispatchers powered by Large
+Language Models (LLMs). Sim911 enhances training through three key technical
+innovations: (1) knowledge construction, which utilizes archived 9-1-1 call
+data to generate simulations that closely mirror real-world scenarios; (2)
+context-aware controlled generation, which employs dynamic prompts and vector
+bases to ensure that LLM behavior aligns with training objectives; and (3)
+validation with looped correction, which filters out low-quality responses and
+refines the system performance.
+
+
+
+
+ + ♻ ☆ TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual + Similarity ICASSP 2025 +
+ +
+ Language models based on deep neural networks are vulnerable to textual
+adversarial attacks. While rich-resource languages like English are receiving
+focused attention, Tibetan, a cross-border language, is gradually being studied
+due to its abundant ancient literature and critical language strategy.
+Currently, there are several Tibetan adversarial text generation methods, but
+they do not fully consider the textual features of Tibetan script and
+overestimate the quality of generated adversarial texts. To address this issue,
+we propose a novel Tibetan adversarial text generation method called TSCheater,
+which considers the characteristic of Tibetan encoding and the feature that
+visually similar syllables have similar semantics. This method can also be
+transferred to other abugidas, such as Devanagari script. We utilize a
+self-constructed Tibetan syllable visual similarity database called TSVSDB to
+generate substitution candidates and adopt a greedy algorithm-based scoring
+mechanism to determine substitution order. After that, we conduct the method on
+eight victim language models. Experimentally, TSCheater outperforms existing
+methods in attack effectiveness, perturbation magnitude, semantic similarity,
+visual similarity, and human acceptance. Finally, we construct the first
+Tibetan adversarial robustness evaluation benchmark called AdvTS, which is
+generated by existing methods and proofread by humans.
+
+
+
+ comment: Pre-Camera-Ready Version; Accepted at ICASSP 2025
+
+
+
+ + ♻ ☆ Subtopic-aware View Sampling and Temporal Aggregation for Long-form + Document Matching +
+ +
+ Long-form document matching aims to judge the relevance between two documents
+and has been applied to various scenarios. Most existing works utilize
+hierarchical or long context models to process documents, which achieve coarse
+understanding but may ignore details. Some researchers construct a document
+view with similar sentences about aligned document subtopics to focus on
+detailed matching signals. However, a long document generally contains multiple
+subtopics. The matching signals are heterogeneous from multiple topics.
+Considering only the homologous aligned subtopics may not be representative
+enough and may cause biased modeling. In this paper, we introduce a new
+framework to model representative matching signals. First, we propose to
+capture various matching signals through subtopics of document pairs. Next, We
+construct multiple document views based on subtopics to cover heterogeneous and
+valuable details. However, existing spatial aggregation methods like attention,
+which integrate all these views simultaneously, are hard to integrate
+heterogeneous information. Instead, we propose temporal aggregation, which
+effectively integrates different views gradually as the training progresses.
+Experimental results show that our learning framework is effective on several
+document-matching tasks, including news duplication and legal case retrieval.
+
+
+
+
+ + ♻ ☆ Is Parameter Collision Hindering Continual Learning in LLMs? +
+
+
+
+
+
+
+
+ Shuo Yang, Kun-Peng Ning, Yu-Yang Liu, Jia-Yu Yao, Yong-Hong Tian, Yi-Bing Song, Li Yuan
+
+
+ Large Language Models (LLMs) often suffer from catastrophic forgetting when
+learning multiple tasks sequentially, making continual learning (CL) essential
+for their dynamic deployment. Existing state-of-the-art (SOTA) methods, such as
+O-LoRA, typically focus on constructing orthogonality tasks to decouple
+parameter interdependence from various domains.In this paper, we reveal that
+building non-collision parameters is a more critical factor in addressing CL
+challenges. Our theoretical and experimental analyses demonstrate that
+non-collision parameters can provide better task orthogonality, which is a
+sufficient but unnecessary condition. Furthermore, knowledge from multiple
+domains will be preserved in non-collision parameter subspaces, making it more
+difficult to forget previously seen data. Leveraging this insight, we propose
+Non-collision Low-Rank Adaptation (N-LoRA), a simple yet effective approach
+leveraging low collision rates to enhance CL in LLMs. Experimental results on
+multiple CL benchmarks indicate that N-LoRA achieves superior performance
+(+2.9), higher task orthogonality (*4.1 times), and lower parameter collision
+(*58.1 times) than SOTA methods.
+
+
+
+
+ + ♻ ☆ MiMoTable: A Multi-scale Spreadsheet Benchmark with Meta Operations for + Table Reasoning COLING 2025 +
+ +
+ Extensive research has been conducted to explore the capability of Large
+Language Models (LLMs) for table reasoning and has significantly improved the
+performance on existing benchmarks. However, tables and user questions in
+real-world applications are more complex and diverse, presenting an unignorable
+gap compared to the existing benchmarks. To fill the gap, we propose a
+\textbf{M}ult\textbf{i}-scale spreadsheet benchmark with \textbf{M}eta
+\textbf{o}perations for \textbf{Table} reasoning, named as MiMoTable.
+Specifically, MiMoTable incorporates two key features. First, the tables in
+MiMoTable are all spreadsheets used in real-world scenarios, which cover seven
+domains and contain different types. Second, we define a new criterion with six
+categories of meta operations for measuring the difficulty of each question in
+MiMoTable, simultaneously as a new perspective for measuring the difficulty of
+the existing benchmarks. Experimental results show that Claude-3.5-Sonnet
+achieves the best performance with 77.4\% accuracy, indicating that there is
+still significant room to improve for LLMs on MiMoTable. Furthermore, we grade
+the difficulty of existing benchmarks according to our new criteria.
+Experiments have shown that the performance of LLMs decreases as the difficulty
+of benchmarks increases, thereby proving the effectiveness of our proposed new
+criterion.
+
+
+
+ comment: Accepted by COLING 2025
+
+
+
+ + ♻ ☆ Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark + for Evaluating Long-Context Large Language Models COLING 2025 +
+ +
+ Despite recent efforts to develop large language models with robust
+long-context capabilities, the lack of long-context benchmarks means that
+relatively little is known about their performance. To alleviate this gap, in
+this paper, we propose \textbf{Counting-Stars}, a multi-evidence,
+position-aware, and scalable benchmark designed to evaluate the multi-evidence
+retrieval capabilities of long-context LLMs. \textbf{Counting-Stars} comprises
+two counting-based multiple pieces of evidence retrieval sub-tasks: searching
+and reasoning. Using Counting-Stars, we conduct experiments to evaluate several
+long-context LLMs, including GPT-4 Turbo, Gemini 1.5 Pro, Claude3 Opus, GLM-4,
+and Moonshot-v1. Extensive experimental results demonstrate that Gemini 1.5 Pro
+achieves the best overall results, while GPT-4 Turbo exhibits the most stable
+performance across various tasks. Furthermore, our analysis of these LLMs,
+which have been extended to handle long-context scenarios, indicates that
+significant room for improvement remains as the length of the input context and
+the complexity of the tasks increase.
+
+
+
+ comment: Accepted by COLING 2025
+
+
+
+ + ♻ ☆ Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for + Code Generation with Lookahead Planning +
+ +
+ Fill-in-the-Middle (FIM) has become integral to code language models,
+enabling generation of missing code given both left and right contexts.
+However, the current FIM training paradigm, which reorders original training
+sequences and then performs regular next-token prediction (NTP), often leads to
+models struggling to generate content that aligns smoothly with the surrounding
+context. Crucially, while existing works rely on rule-based post-processing to
+circumvent this weakness, such methods are not practically usable in
+open-domain code completion tasks as they depend on restrictive,
+dataset-specific assumptions (e.g., generating the same number of lines as in
+the ground truth). Moreover, model performance on FIM tasks deteriorates
+significantly without these unrealistic assumptions.
+ We hypothesize that NTP alone is insufficient for models to learn effective
+planning conditioned on the distant right context, a critical factor for
+successful code infilling. To overcome this, we propose Horizon-Length
+Prediction (HLP), a novel training objective that teaches models to predict the
+number of remaining middle tokens (i.e., horizon length) at each step. HLP
+advances FIM with lookahead planning, enabling models to inherently learn
+infilling boundaries for arbitrary left and right contexts without relying on
+dataset-specific post-processing. Our evaluation across different models and
+sizes shows that HLP significantly improves FIM performance by up to 24%
+relatively on diverse benchmarks, across file-level and repository-level, and
+without resorting to unrealistic post-processing methods. Furthermore, the
+enhanced planning capability gained through HLP boosts model performance on
+code reasoning. Importantly, HLP only incurs negligible training overhead and
+no additional inference cost, ensuring its practicality for real-world
+scenarios.
+
+
+