Papers by Pan Liu
Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs (2026.acl-long)
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Yu Li, Xiaoran Shang, Qizhi Pei, Yun Zhu, Xin Gao, Honglin Lin, Zhanping Zhong, Zhuoshi Pan, Zheng Liu, Xiaoyang Wang, Conghui He, Dahua Lin, Feng Zhao, Lijun Wu
| Challenge: | High-quality post-training data is the primary engine driving LLM capabilities . datasets are often treated as isolated artifacts, overlooking their true developmental context . |
| Approach: | They propose a framework to reconstruct the evolutionary graph of dataset development using data lineage. |
| Outcome: | The proposed framework characterizes domain-specific structural patterns in Math-oriented datasets and general-domain corpora. |
WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)
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Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei
| Challenge: | Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions. |
| Approach: | They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach . |
| Outcome: | The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size. |
Towards Explainable Computerized Adaptive Testing with Large Language Model (2024.findings-emnlp)
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| Challenge: | Existing methods focus on minimizing the number of questions required to assess ability, lacking clear and reliable explanations for the question selection process. |
| Approach: | They propose to use large language models to enhance computer adaptive testing (CAT) by providing human-like interpretability and explanations. |
| Outcome: | The proposed agent-based CAT performs comparably or superior to traditional CAT methods in accuracy and significantly improves student trust and satisfaction. |
Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings (2025.acl-long)
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| Challenge: | Text embedding models are used for various natural language processing tasks such as sentiment analysis, text clustering, and content-based information retrieval. |
| Approach: | They propose a synthesis framework that leverages large language models to generate diverse negative samples with varying levels of similarity with the query. |
| Outcome: | The proposed framework achieves state-of-the-art performance surpassing existing synthesis strategies with synthetic data and when combined with public retrieval datasets. |
CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models (2024.findings-emnlp)
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Yaojia Lv, Haojie Pan, Zekun Wang, Jiafeng Liang, Yuanxing Liu, Ruiji Fu, Ming Liu, Zhongyuan Wang, Bing Qin
| Challenge: | Recent advances in large language models (LLMs) focus on replicating human cognition in specific contexts, overlooking the inherently dynamic nature of cognition. |
| Approach: | They propose a task to assess cognitive dynamics of large language models (LLMs) they introduce a benchmark and two evaluation metrics to validate the benchmark and evaluate it through participant surveys. |
| Outcome: | The proposed task overcomes the limitations of existing methods and is available for download. |
AGD: Adversarial Game Defense Against Jailbreak Attacks in Large Language Models (2025.acl-long)
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Shilong Pan, Zhiliang Tian, Zhen Huang, Wanlong Yu, Zhihua Wen, Xinwang Liu, Kai Lu, Minlie Huang, Dongsheng Li
| Challenge: | Existing defenses, including post-training alignment and prompt engineering, struggle with adaptability to out-of-distribution (OOD) attacks. |
| Approach: | They propose an adversarial game-based defense method that dynamically adjusts LLMs’ internal representations to achieve a balanced trade-off between helpfulness and harmlessness. |
| Outcome: | The proposed method improves LLMs’ safety over all baselines. |
AppBench: Planning of Multiple APIs from Various APPs for Complex User Instruction (2024.emnlp-main)
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| Challenge: | Existing state-of-the-art Large Language Models (LLMs) still cannot perform well in this situation even with the help of in-context learning and finetuning. |
| Approach: | They propose a benchmark to evaluate LLMs’ ability to plan and execute multiple APIs from various sources in order to complete the user’s task. |
| Outcome: | The proposed benchmarks show that the existing state-of-the-art LLMs still cannot perform well in this situation even with in-context learning and finetuning. |
Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot (2025.findings-emnlp)
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| Challenge: | In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs). |
| Approach: | They introduce CoT to exemplars of ICL to enhance the reasoning capability . however, it remains unclear whether CoT exemplar is still beneficial for recent, stronger models in such tasks. |
| Outcome: | The enhanced exemplars fail to improve the model’s reasoning performance, despite being constructed using answers from advanced models such as Qwen2.5-Max and DeepSeek-R1. |
BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks (2026.acl-long)
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| Challenge: | Existing supervised defense methods rely on labeled malicious agents to train a supervised model of malicious behavior. |
| Approach: | They propose an unsupervised defense method that learns without requiring any attack-specific labels or prior knowledge of malicious behaviors. |
| Outcome: | The proposed method detects diverse attack types across MAS with various communication patterns while maintaining superior generalizability compared to baselines. |
Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding (2023.findings-emnlp)
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| Challenge: | Existing work on temporal sentence grounding rely on expensive video-query paired annotations . despite this, there are no ground-truth annotations in the current work . |
| Approach: | They propose to use paired video-query and segment boundary annotations to generate temporal sentence grounding without training. |
| Outcome: | The proposed model outperforms existing unsupervised methods and beats supervised ones on two challenging datasets. |
Misalignment Attack on Text-to-Image Models via Text Embedding Optimization and Inversion (2025.findings-emnlp)
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| Challenge: | Text embedding is a key component of modern NLP models but also poses additional risks. |
| Approach: | They propose a framework that optimizes embeddings and inverts them to obtain misaligned prompts. |
| Outcome: | The proposed framework exploits the continuity and distribution characteristics of text embeddings to obtain misaligned prompts of discrete tokens. |
DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have emerged as prominent foundation models for diverse applications due to their outstanding ability to understand and generate humanlike text. |
| Approach: | They propose a dynamic decision-making framework that categorizes tasks into two distinct pathways: 'Fast' and 'Slow' they propose 'self-consistency' strategy to replace the straight-forward decoding method used in COT prompting . |
| Outcome: | The proposed method achieves more than 3% increase in accuracy with lower cost on five popular reasoning benchmarks. |
Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis (2026.acl-long)
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Zehua Pei, Hui-Ling Zhen, Lancheng Zou, Xianzhi Yu, Wulong Liu, Sinno Jialin Pan, Mingxuan Yuan, Bei Yu
| Challenge: | Large language models (LLMs) are fast but require expensive pre-training . a new approach to scale large language models into MoEs reduces inference costs . |
| Approach: | They propose an analytical post-training framework that rapidly restructures FFNs into sparse MoE architectures using only a small calibration dataset. |
| Outcome: | The proposed framework outperforms existing methods on a small calibration dataset. |
Cool-Fusion: Fuse Large Language Models without Training (2025.acl-long)
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| Challenge: | Cool-Fusion is a simple yet effective approach to combine two or more heterogeneous large language models . |
| Approach: | They propose a method that fuses the knowledge of two or more heterogeneous large language models to leverage complementary strengths. |
| Outcome: | The proposed method increases accuracy from three strong source LLMs on GSM8K by 17.4%. |
mCLIP: Multilingual CLIP via Cross-lingual Transfer (2023.acl-long)
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Guanhua Chen, Lu Hou, Yun Chen, Wenliang Dai, Lifeng Shang, Xin Jiang, Qun Liu, Jia Pan, Wenping Wang
| Challenge: | Existing multilingual vision-language pretrained models are biased towards English due to the lack of sufficient non-English image-text pairs. |
| Approach: | They propose to train a retrieval-efficient dual-stream multilingual VLP model by aligning CLIP model and a multilingual text encoder through a novel Triangle Cross-modal Knowledge Distillation method. |
| Outcome: | Empirical results show that mCLIP achieves new state-of-the-art performance for both zero-shot and finetuned multilingual image-text retrieval tasks. |
ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch (2026.acl-long)
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Zheng Liu, Honglin Lin, Xiaoyang Wang, Xin Gao, Yu Li, Mengzhang Cai, Yun Zhu, Zhanping Zhong, Qizhi Pei, Zhuoshi Pan, Xiaoran Shang, Conghui He, Bin Cui, Wentao Zhang, Lijun Wu
| Challenge: | Existing open-source vision language models lack high-quality training data for chart reasoning . current models are simplistic and repetitive, while associated QA pairs are prone to hallucinations . |
| Approach: | They propose a framework to synthesize complex charts and reliable reasoning data from scratch. |
| Outcome: | Experimental results show that ChartVerse-8B surpasses existing models in QA and difficulty . lack of high-quality training data hampers development of open-source models . |
Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization (2023.findings-emnlp)
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| Challenge: | Existing methods for prompt tuning can overfit to few-shot training samples, causing overfitting . authors propose a new framework for prompt learning with supervised meta-learning . |
| Approach: | They propose a self-supervised meta-prompt learning framework with MEta-gradient Regularization for few-shot generalization that leverages self-recognized meta-learning with a diverse set of meta-tasks to learn a universal prompt initialization using only unlabeled data. |
| Outcome: | The proposed framework learns a universal prompt initialization for efficient adaptation using only unlabeled data. |
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)
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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen
| Challenge: | Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval. |
| Approach: | They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs). |
| Outcome: | The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models. |
In-the-wild Audio Spatialization with Flexible Text-guided Localization (2025.acl-long)
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| Challenge: | Existing methods for mapping monaural audio to binaural signals lack flexibility and interactive control needed in complex multi-object user-interactive environments. |
| Approach: | They propose a text-guided audio spatialization framework that utilizes diverse text prompts to evaluate binaural audio models. |
| Outcome: | The proposed framework learns binaural differences guided by 3D spatial location and relative position prompts, enhanced with flipped-channel audio. |
A Web Scale Entity Extraction System (2021.findings-emnlp)
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| Challenge: | Existing systems for large-scale entity extraction are limited by the scale and variety of data available on internet platforms. |
| Approach: | They propose to build an entity extraction system for multiple document types at large scale using multi-modal Transformers. |
| Outcome: | The proposed system extracts multiple types of entities from multiple document types at large scale using multi-modal Transformers. |
MMedAgent: Learning to Use Medical Tools with Multi-modal Agent (2024.findings-emnlp)
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Binxu Li, Tiankai Yan, Yuanting Pan, Jie Luo, Ruiyang Ji, Jiayuan Ding, Zhe Xu, Shilong Liu, Haoyu Dong, Zihao Lin, Yixin Wang
| Challenge: | Multi-modal Large Language Models (MLLMs) exhibit limited generality and often fall short when compared to specialized models. |
| Approach: | They propose a multi-modal medical agent that picks the most suitable medical tools based on user inputs. |
| Outcome: | The proposed agent performs better than open-source models and the closed-source model, GPT-4o. |
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) face memory challenges due to the high cost of backpropagation. |
| Approach: | They propose a zeroth-order (ZO) optimization that matches memory usage to inference . they propose scalable and memory-efficient zeroth order (ZE) optimizer that integrates annealed A-GNB gradients with diagonal Hessian estimation and layer-wise clipping as a second-order pre-conditioner. |
| Outcome: | The proposed algorithm outperforms state-of-the-art methods with an average speedup of 20 over MeZO on RoBERTa-large and OPT-1.3B. |
SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition (2025.acl-long)
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Shuangrui Ding, Zihan Liu, Xiaoyi Dong, Pan Zhang, Rui Qian, Junhao Huang, Conghui He, Dahua Lin, Jiaqi Wang
| Challenge: | Creating lyrics and melodies in symbolic format requires expert knowledge of melody and an advanced understanding of lyrics. |
| Approach: | They introduce SongComposer, a music-specialized large language model that can create symbolic lyrics and melodies following instructions. |
| Outcome: | The proposed model outperforms existing models in symbolic song composition tasks. |
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification (2024.naacl-long)
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| Challenge: | Existing self-supervised methods in natural language processing rely on augmentation rules to generate contrastive samples. |
| Approach: | They propose a hierarchy-aware information lossless contrastive learning scheme that uses syntactic information reserved in the input sample and fused during the learning process. |
| Outcome: | The proposed learning scheme is superior to existing methods in hierarchical text classification . the proposed learning system is based on a structure encoder and a text encoder . |
AgentV-RL: Scaling Reward Modeling with Agentic Verifier (2026.findings-acl)
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Jiazheng Zhang, Ziche Fu, Zhiheng Xi, Wenqing Jing, Mingxu Chai, Wei He, Guoqiang Zhang, Chenghao Fan, Chenxin An, Wenxiang Chen, Zhicheng Liu, Haojie Pan, Dingwei Zhu, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to improve LLM reasoning are limited in complex domains and lack external grounding makes verifiers unreliable on computation-intensive tasks. |
| Approach: | They propose a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process. |
| Outcome: | The proposed framework surpasses state-of-the-art ORMs by 25.2% under parallel and sequential TTS. |
Mitigating the Alignment Tax of RLHF (2024.emnlp-main)
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Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang
| Challenge: | Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax. |
| Approach: | They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms. |
| Outcome: | The proposed method achieves the strongest alignment-forging Pareto front among competing methods. |
SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation (2026.findings-acl)
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| Challenge: | Agentic SQL is a framework for multiturn agent learning, but it is limited to single-turn paradigms. |
| Approach: | They propose a framework that provides a universal two-tiered reward mechanism for credit assignment . they propose 'Aggregated Trajectory Reward' to resolve multi-turn credit assignment. |
| Outcome: | The proposed framework outperforms SOTA Arctic-Text2SQL-R1-7B on BIRD and Spider 2.0 using identical models. |
Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks. |
| Approach: | They propose a tri-encoder sequential retriever that models a Markov Decision Process (MDP) this method decomposes the probability of retrieving a set of elements into a sequence of conditional probabilities and allows each retrieval step to be conditioned on previously selected examples. |
| Outcome: | The proposed method outperforms baselines and shows that it can handle multiple pieces of evidence or examples. |
Thinking Before You Speak: A Proactive Test-time Scaling Approach (2025.findings-emnlp)
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| Challenge: | Large Language Models often exhibit deficiencies with complex reasoning tasks, such as maths, due to the discrepancy between human reasoning patterns and those presented in training data. |
| Approach: | They propose to insert insights between consecutive reasoning steps to bridge this gap by generating insights between the next reasoning steps. |
| Outcome: | Experiments on mathematical datasets confirm the effectiveness of the proposed reasoning framework on complex problems. |
DART: Open-Domain Structured Data Record to Text Generation (2021.naacl-main)
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Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
| Challenge: | Data-to-text annotations can be costly when dealing with tables with nontrivial structures. |
| Approach: | They propose a procedure for extracting semantic triples from tables that encodes their structures by exploiting table headers and table title. |
| Outcome: | The proposed method exploits the semantic dependencies between table headers and title to extract semantic triples from tables. |
MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues (2026.findings-acl)
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Yaning Pan, Qianqian Xie, Guohui Zhang, Zekun Moore Wang, Yongqian Wen, Yuanxing Zhang, Haoxuan Hu, Zhiyu Pan, Yibing Huang, Zhidong Gan, Yonghong Lin, An Ping, Shihao Li, Yanghai Wang, Tianhao Peng, Jiaheng Liu
| Challenge: | Existing evaluation benchmarks for Multimodal Large Language Models (MLLMs) focus on single-turn question answering, overlooking the complexity of multi-turn dialogues in real-world scenarios. |
| Approach: | They propose a video understanding benchmark for MLLMs in multi-turn dialogues that assesses six core competencies that focus on perceptivity and interactivity. |
| Outcome: | The MT-Video-Bench evaluates 1,000 multi-turn dialogues from diverse domains and reveals significant performance discrepancies and limitations in handling multi-turned video dialogues. |
OctoTools: A Multi-Agent Framework with Extensible Tools for Complex Reasoning (2026.acl-long)
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| Challenge: | Existing prompting methods for large language models (LLMs) are restricted to specialized domains, limited tool types, or require additional training data. |
| Approach: | They propose a training-free, user-friendly, and easily extensible multi-agent framework designed to tackle complex reasoning across diverse domains. |
| Outcome: | The proposed framework outperforms AutoGen, GPT-Functions, and LangChain by up to 10.6% when given the same set of tools. |
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems (2021.naacl-demos)
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Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel-Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien-Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, Eddie Wang
| Challenge: | Traditional goal-oriented dialogue systems require annotations which are hard to obtain for every new domain, limiting scalability. |
| Approach: | They propose a data-driven approach to building goal-oriented dialogue systems . they use a seed dialogue simulator to generate annotated conversations instead of collecting annotations . |
| Outcome: | The proposed system improves turn-level action signature prediction accuracy by 50% . the system is scalable, extensible and data efficient . |
MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval (2026.acl-demo)
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| Challenge: | MED-COPILOT is an interactive research prototype for evidence-aware clinical reasoning . large language models (LLMs) are prone to hallucinations and lack verifiable evidence grounding . |
| Approach: | They propose a system that integrates GraphRAG and semantic-keyword similar-patient retrieval to support transparent clinical reasoning. |
| Outcome: | The proposed system outperforms baseline and standard RAGs on clinical note completion and medical question answering. |
WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents (2025.findings-naacl)
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| Challenge: | Existing methods focus on distinguishing fully watermarked text from non-watermarked text, overlooking real-world scenarios where LLMs generate only brief segments within longer documents. |
| Approach: | They propose a method to detect watermarked segments in large documents using an anomaly extraction method and a local traversal. |
| Outcome: | The proposed method achieves a superior balance between detection accuracy and computational efficiency. |
PlanGenLLMs: A Modern Survey of LLM Planning Capabilities (2025.acl-long)
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| Challenge: | Existing studies have focused on developing LLMs to automate complex planning tasks. |
| Approach: | They propose to provide a comprehensive overview of current LLM planners to fill this gap . they examine performance criteria including completeness, executability, optimality, representation, generalization, and efficiency . |
| Outcome: | The proposed survey examines performance criteria for LLM planners and highlights their strengths and weaknesses. |
TS-SQL: Test-driven Self-refinement for Text-to-SQL (2025.findings-emnlp)
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| Challenge: | null |
| Approach: | null |
| Outcome: | null |
Making LLMs Better Many-to-Many Speech-to-Text Translators with Curriculum Learning (2025.acl-long)
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Yexing Du, Youcheng Pan, Ziyang Ma, Bo Yang, Yifan Yang, Keqi Deng, Xie Chen, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Existing studies on English-centric translation tasks have focused on multimodal large language models, but the exploration of many-to-many translation is limited by the scarcity of parallel data. |
| Approach: | They propose a three-stage curriculum learning strategy that leverages the machine translation capabilities of large language models and adapts them to S2TT tasks. |
| Outcome: | The proposed strategy achieves state-of-the-art average performance in 1514 language pairs, requiring fewer than 10 hours of speech data per language to achieve competitive results. |
A Lifelong Multilingual Multi-granularity Semantic Alignment Approach via Maximum Co-occurrence Probability (2024.lrec-main)
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| Challenge: | Existing methods to mask and predict tokens in multilingual text limit multilingual interaction . |
| Approach: | They propose a lifelong multilingual multi-granularity semantic alignment approach which continuously extracts massive aligned linguistic units from noisy data via a maximum co-occurrence probability algorithm. |
| Outcome: | The proposed approach improves translation performance on WMT14 18 benchmarks in twelve directions. |
ProBench: Judging Multimodal Foundation Models on Open-ended Multi-domain Expert Tasks (2025.findings-acl)
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| Challenge: | Solving expert-level multimodal tasks requires strong user query understanding, domain-specific knowledge, and advanced reasoning abilities. |
| Approach: | They propose a benchmark of open-ended user queries encapsulating professional expertise and advanced reasoning. |
| Outcome: | The proposed benchmark is publicly accessible at TBC. |
GUIDE: Towards Scalable Advising for Research Ideas (2026.acl-long)
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| Challenge: | Existing systems that provide detailed, constructive feedback on academic papers struggle with review fidelity. |
| Approach: | They explore factors that underlie the development of robust advising systems . large language models have shown remarkable progress in tasks from text generation to code synthesis . |
| Outcome: | The proposed model outperforms general-purpose language models in acceptance rates for self-ranked top-30% submissions to ICLR 2025. |
InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model (2025.findings-acl)
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Yuhang Zang, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Ziyu Liu, Shengyuan Ding, Shenxi Wu, Yubo Ma, Haodong Duan, Wenwei Zhang, Kai Chen, Dahua Lin, Jiaqi Wang
| Challenge: | Despite the promising performance of Large Vision Language Models, they sometimes generate incorrect outputs. |
| Approach: | They propose a multi-modal reward model that aligns LVLMs with human preferences. |
| Outcome: | The proposed model achieves excellent results on the latest multi-modal reward model benchmark and shows competitive performance on text-only reward model. |
KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024.acl-long)
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Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Lixiang Lixiang, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | None. None.. None! |
| Approach: | None. None.. None! |
| Outcome: | None. None. No. : |
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge (2022.emnlp-main)
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Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou
| Challenge: | Existing approaches to text-to-SQL require domain knowledge to parse expert questions into SQL queries. |
| Approach: | They propose a framework to leverage domain knowledge during parsing by building a new benchmark KnowSQL with domain-specific questions. |
| Outcome: | The proposed framework improves the performance of the proposed benchmark by 28.2%. |
Active Prompting with Chain-of-Thought for Large Language Models (2024.acl-long)
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| Challenge: | Existing methods to annotate large language models rely on a fixed set of human-annotated exemplars, which are not always the most effective for different tasks. |
| Approach: | They propose a method to adapt large language models to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning) they introduce several metrics to characterize uncertainty so as to select the most uncertain questions for annotation. |
| Outcome: | The proposed method significantly improves performance on eight complex reasoning tasks. |
Towards Provably Secure Generative AI: Reliable Consensus Sampling (2026.findings-acl)
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Yu Cui, Hang Fu, Sicheng Pan, Zhuoyu Sun, Yifei Liu, Yuhong Nie, Bo Ran, Baohan Huang, Xufeng Zhang, Haibin Zhang, Cong Zuo, Licheng Wang
| Challenge: | Existing research on generative AI security is driven by mutually reinforcing attack and defense methodologies grounded in empirical experience. |
| Approach: | They propose a new algorithm that uses a random sampling algorithm to control risk. |
| Outcome: | The proposed algorithm improves robustness and utility while maintaining latency comparable to existing algorithms. |
CausalEval: Towards Better Causal Reasoning in Language Models (2025.naacl-long)
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Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan
| Challenge: | Large language models (LLMs) have been used for a variety of tasks, including problem-solving, decision-making, and understanding of the world. |
| Approach: | They propose a review of existing methods aimed at enhancing LMs for causal reasoning . they categorize existing methods as reasoning engines or as helpers providing knowledge or data to traditional methods . |
| Outcome: | The proposed methods perform better than existing methods on a range of tasks. |
Plum: Prompt Learning using Metaheuristics (2024.findings-acl)
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Rui Pan, Shuo Xing, Shizhe Diao, Wenhe Sun, Xiang Liu, KaShun Shum, Jipeng Zhang, Renjie Pi, Tong Zhang
| Challenge: | Recent advances in prompt learning have led to a need for general prompt optimization methods. |
| Approach: | They propose a branch of discrete non-convex optimization methods with over 100 options as a promising approach to prompt learning. |
| Outcome: | The proposed methods can be used to discover more human-understandable prompts that were previously unknown in reasoning and image generation tasks. |
Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve Framework (2025.acl-long)
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Jundong Xu, Hao Fei, Meng Luo, Qian Liu, Liangming Pan, William Yang Wang, Preslav Nakov, Mong-Li Lee, Wynne Hsu
| Challenge: | Existing systems fail to fully leverage the structure of logical tasks throughout the reasoning process, causing bottlenecks in efficiency and efficacy. |
| Approach: | They propose a logic-complete reasoning framework, Aristotle, which integrates symbolic expressions and logical rules into the entire reasoning process. |
| Outcome: | The proposed framework outperforms state-of-the-art reasoning frameworks in accuracy and efficiency. |
No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning (2026.acl-long)
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Zhicong Li, Lingjie Jiang, Yulan Hu, Xingchen Zeng, Yixia Li, Xiangwen Zhang, Guanhua Chen, Zheng Pan, Xin Li, Yong Liu
| Challenge: | Current methods for training Large Language Model agents rely on static or offline critic models, which fail to adapt as the policy evolves. |
| Approach: | They propose a framework that integrates a critique and a policy to optimize the policy and critic through a synchronized co-evolutionary loop. |
| Outcome: | The proposed framework yields more stable training and higher long-horizon task success across open-world environments. |
Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen (2020.acl-main)
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| Challenge: | a curse of knowledge can impede communication between experts and laymen. |
| Approach: | They propose a task of expertise style transfer to automate the communication between experts and laymen using an annotated dataset. |
| Outcome: | The proposed task simplifies professional language and improves accuracy and expertise level of laymen descriptions using simple words. |
LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines (2026.findings-acl)
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| Challenge: | Pretrained language models (PLMs) provide strong semantic representations but are costly and opaque. |
| Approach: | They propose a framework that transfers pretrained language models into symbolic form and integrates them into symbolic models. |
| Outcome: | The proposed framework improves interpretability and accuracy across multiple text classification tasks while remaining fully symbolic and efficient. |
SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation (2025.acl-long)
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| Challenge: | Adaptive Retrieval-Augmented Generation (RAG) is an effective strategy to alleviate hallucination of large language models (LLMs). |
| Approach: | They propose a novel adaptive RAG model that extracts self-aware uncertainty of large language models from their internal states and invokes retrieval accordingly. |
| Outcome: | The proposed model outperforms existing adaptive RAG methods on complex and simple Question Answering datasets. |
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in aligning with user intentions. |
| Approach: | They develop local and global explanation methods and a feed-forward-based method for input-output attribution to investigate the impact of instruction tuning on user intentions. |
| Outcome: | The proposed method compares explanations from pre-trained and instruction-tuned models . it empowers LLMs to recognize the instruction parts of user prompts, it encourages response generation . |
Table-R1: Region-based Reinforcement Learning for Table Understanding (2026.findings-acl)
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Zhenhe Wu, Jian Yang, Zhongjiang He, Changzai Pan, Jiaheng Liu, Xianjie Wu, Yu Zhao, Shuangyong Song, Yongxiang Li, Zhoujun Li, Xuelong Li
| Challenge: | Tables are a widely used data format that poses unique challenges for language models due to their structured row-column interactions. |
| Approach: | They propose a region-based reinforcement learning approach that integrates region evidence into reasoning steps. |
| Outcome: | The proposed method outperforms baseline models on three benchmark datasets and significantly reduces the reasoning token consumption by 67.5%. |
VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making (2025.emnlp-main)
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| Challenge: | Recent large-scale pretrained models are built upon a multi-input single-output paradigm . tasks compete for a shared output channel, creating mutual exclusion effects . |
| Approach: | They propose a multi-input single-output (MISO) paradigm for large pretrained models . they propose unified training framework that enables concurrent multi-task outputs . |
| Outcome: | Experiments on autonomous driving platform show that MIMO-VLA outperforms state-of-the-art models in MIMO settings. |
Feedback to Reasoning: LLM-Assisted Molecular Optimization with Domain Feedback and Historical Reasoning (2026.findings-acl)
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Wenhan Gao, Xiran Fan, Chin-Chia Michael Yeh, Jiarui Sun, Yuzhong Chen, Menghai Pan, Mahashweta Das, Yi Liu
| Challenge: | Existing methods for molecular optimization do not leverage domain feedback and historical knowledge with reasoning traces and chemical insights. |
| Approach: | They propose a conversational molecular optimization pipeline that enables LLMs to accumulate and retrieve past actions, rationales, and feedback. |
| Outcome: | The proposed framework transforms LLMs from passive text generators into agentic experts that learn both actions and reasoning from experience. |
Enhanced Language Representation with Label Knowledge for Span Extraction (2021.emnlp-main)
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| Challenge: | Existing approaches to extract text spans from plain text do not fully exploit label knowledge. |
| Approach: | They propose a model to integrate label knowledge into text representations by encoding texts and annotations independently and then integrating label knowledge with an elaborate-designed semantics fusion module. |
| Outcome: | The proposed model achieves state-of-the-art performance on four benchmarks and reduces training time and inference time by 76% and 77% on average compared with the existing paradigm. |
MedWriter: Knowledge-Aware Medical Text Generation (2020.coling-main)
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Youcheng Pan, Qingcai Chen, Weihua Peng, Xiaolong Wang, Baotian Hu, Xin Liu, Junying Chen, Wenxiu Zhou
| Challenge: | Recent studies focus on the information of unstructured text rather than structured information of the knowledge graph. |
| Approach: | They propose a knowledge-aware text generation model for medical domains that incorporates knowledge graphs into the model to improve the quality of generated text. |
| Outcome: | The proposed model improves the quality of generated text and has robust superiority over other methods. |
Think Faster Than Words: Efficient LLM Chain-of-Thought Reasoning via Dynamic Shortcut Decoding (2026.acl-long)
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| Challenge: | Existing methods that prune or employ early stopping to reduce latency often compromise reasoning reliability. |
| Approach: | They propose a shortcut decoding framework that integrates probes over internal hidden states with step-level entropy to detect convergence of reasoning during generation and adaptively selects between a fast-exit path and a stability-verified path to remove redundant steps while preserving answer correctness. |
| Outcome: | The proposed framework reduces token usage by approximately 35% and maintains accuracy comparable to full CoT decoding. |
CMMaTH: A Chinese Multi-modal Math Skill Evaluation Benchmark for Foundation Models (2025.coling-main)
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Zhongzhi Li, Ming-Liang Zhang, Pei-Jie Wang, Jian Xu, Rui-Song Zhang, Yin Fei, Zhi-Long Ji, Jin-Feng Bai, Zhen-Ru Pan, Jiaxin Zhang, Cheng-Lin Liu
| Challenge: | Large language models excel in various language tasks, while large multimodal models effectively handle visual-language problems. |
| Approach: | They propose to use a multimodal multimodal model evaluation benchmark to evaluate model performance in Chinese K12 classrooms. |
| Outcome: | The proposed model evaluation tool is integrated with the CMMaTH dataset. |
d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models (2026.acl-long)
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Leyi Pan, Shuchang Tao, Yunpeng Zhai, Zheyu Fu, Liancheng Fang, Minghua He, Lingzhe Zhang, Zhaoyang Liu, Bolin Ding, Aiwei Liu, Lijie Wen
| Challenge: | Existing RL methods suffer from reliability bottlenecks due to reward sparsity and intractable computations . d-TreeRPO provides fine-grained and verifiable step-wise reward signals . |
| Approach: | They propose a reliable reinforcement learning framework for diffusion large language models that leverages tree-structured rollouts and bottom-up advantage computation based on verifiable outcome rewards. |
| Outcome: | The proposed framework outperforms baseline models and achieves significant improvements across reasoning benchmarks. |
Towards Universal Debiasing for Language Models-based Tabular Data Generation (2025.findings-emnlp)
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| Challenge: | Existing large language models have exacerbated fairness issues in tabular data generation . inherent historical biases in tabulated data cause LLMs to exacerbate fairness problems . |
| Approach: | They propose a universal debiasing framework that minimizes group-level dependencies . it leverages the autoregressive structure and analytic sampling distributions of LLM-based tabular data generators . |
| Outcome: | The proposed framework minimizes group-level dependencies while reducing mutual information between advantaged and protected attributes. |
FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling (2025.acl-long)
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Weilin Zhao, Tengyu Pan, Xu Han, Yudi Zhang, Sun Ao, Yuxiang Huang, Kaihuo Zhang, Weilun Zhao, Yuxuan Li, Jie Zhou, Hao Zhou, Jianyong Wang, Maosong Sun, Zhiyuan Liu
| Challenge: | Speculative sampling is an efficient way to accelerate the auto-regressive generation process of large language models. |
| Approach: | They propose a frequency-ranked speculative sampling framework that optimizes draft candidate selection through vocabulary space compression. |
| Outcome: | Experiments show that FR-Spec reduces LM Head computation overhead by 75% while ensuring the equivalence of the final output distribution. |
Don’t Act Blindly: Robust GUI Automation via Action-Effect Verification and Self-Correction (2026.acl-long)
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Yuzhe Zhang, Xianwei Xue, Xingyong Wu, Mengke Chen, Chen Liu, Xinran He, Run Shao, Feiran Liu, Huanmin Xu, Qiutong Pan, Haiwei Wang
| Challenge: | Existing GUI agents assume deterministic environment responses, generating actions without verifying whether previous operations succeeded. |
| Approach: | They propose a GUI agent that explicitly models action outcomes and recovery under noisy environments. |
| Outcome: | The proposed agent reduces failure loops and improves recovery success in noisy environments while maintaining competitive standard task performance. |
KELE: A Multi-Agent Framework for Structured Socratic Teaching with Large Language Models (2025.findings-emnlp)
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| Challenge: | Socratic teaching places high demands on teachers’ expertise and real-time feedback capabilities, making it difficult to scale in large educational settings. |
| Approach: | They propose a multi-agent framework for structured Socratic teaching with LLMs that integrates a structured SocRule and a consultant-teacher collaborative teaching mechanism. |
| Outcome: | The proposed framework outperforms existing LLMs in natural language generation and dialogue comprehension in the classroom. |
Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalyst (2025.findings-acl)
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| Challenge: | Recent studies have demonstrated that inference-time scaling increases performance of Large Language Models (LLMs) in various reasoning tasks such as mathematics and complex question answering by increasing the length of Chain-of-Thought (CoT). |
| Approach: | They propose a model which synthesizes longer CoT data and iteratively improves performance through self-training by incorporating a few demonstration examples. |
| Outcome: | The proposed model achieves an average improvement of more than +2.5 points across five reasoning tasks: MMLU, GSM8K, ARC-C, HellaSwag, and BBH on two backbone models. |
Augmenting Legal Judgment Prediction with Contrastive Case Relations (2022.coling-1)
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| Challenge: | Existing legal judgment prediction methods only consider one case fact description as input, which may not fully utilize information in the data such as case relations and frequency. |
| Approach: | They propose a new perspective that introduces some contrastive case relations to construct case triples as input and a corresponding judgment prediction framework with case triple modeling. |
| Outcome: | The proposed framework can be used to refine encoding and decoding processes using three customized modules on two public datasets. |
MCGA: A Multi-task Classical Chinese Literary Genre Audio Corpus (2026.findings-acl)
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Yexing Du, Kaiyuan Liu, Bihe Zhang, Youcheng Pan, Bo Yang, Liangyu Huo, Xiyuan Zhang, Jian Xie, Daojing He, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Multimodal Large Language Models (MLLMs) have advanced Chinese Classical Studies (CCS) but the audio dimension of CCS remains underexplored due to a lack of high-quality, domain-specific audio corpora. |
| Approach: | They propose a 119-hour audio corpus comprising 22,000 audio samples to bridge this gap . it encompasses a diverse range of literary genres across six tasks . |
| Outcome: | The proposed corpus encompasses a diverse range of literary genres across six tasks: Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Speech Emotion Captioning (SEC), Spoken Question Answering ( SQA), Speech Understanding (SU), and Speech Reasoning (SR). |
ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation (2025.findings-acl)
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| Challenge: | Existing benchmarks lack the ability to automatically evaluate from users’ perspective and lack the explainability of the results of LLM agents’ code generation capabilities. |
| Approach: | They propose a new benchmark for LLM agents' automated evaluation by simulating user interaction. |
| Outcome: | The proposed benchmark can evaluate the generated projects by user interaction simulation and by code similarity through existing objective indicators. |
Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation? (2025.acl-long)
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| Challenge: | Large Language Model (LLM) watermarking is radioactive and enables the detection of watermarks inherited by student models when trained on the outputs of watermarked teacher models. |
| Approach: | They propose two types of watermark removal attacks that allow student models to perform untraceable knowledge distillation while avoiding watermark inheritance. |
| Outcome: | The proposed attacks eliminate inherited watermarks while maintaining knowledge transfer efficiency and low computational overhead. |
VortexPIA: Indirect Prompt Injection Attack against LLMs for Efficient Extraction of User Privacy (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) have been widely deployed in Conversational AIs . however, the methods proposed in the study rely on a white-box setting . |
| Approach: | They propose an indirect prompt injection attack that induces privacy extraction in LLMs . they use token-efficient data containing false memories to inject LLM data . |
| Outcome: | The proposed method outperforms baselines and achieves state-of-the-art performance. |
Bridge-Coder: Transferring Model Capabilities from High-Resource to Low-Resource Programming Language (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel at generating code for high-resource programming languages (HRPLs) however, they struggle significantly with low-resourced programming languages such as D, exacerbating the digital divide. |
| Approach: | They propose a method to generate LRPL data using LLM's general knowledge, HRPL proficiency, and in-context learning capabilities. |
| Outcome: | The proposed method improves on R, D, Racket, and Bash, while maintaining the same quality. |
Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification (2022.coling-1)
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| Challenge: | Existing methods for hierarchical text classification are lacking in the field of natural language processing. |
| Approach: | They propose a hierarchy-aware T5 model with path-adaptive attention mechanism to exploit hierarchical dependency across different levels. |
| Outcome: | The proposed model outperforms state-of-the-art models especially in Macro-F1 and low Macro. |
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes Under Herd Behavior (2025.acl-long)
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| Challenge: | relying on authentic data for Supervised Fine-Tuning (SFT) is costly and expensive. |
| Approach: | They propose a framework that constructs high-quality SFT datasets by leveraging theoretical solutions to similar and simple optimal investment problems rather than the complex scenarios. |
| Outcome: | The proposed framework achieves faster parameter convergence than using real-user data, suggesting superior learning efficiency. |
Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment (2020.emnlp-main)
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| Challenge: | Entity alignment (EA) aims at building a Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. |
| Approach: | They propose to use an attributed value encoder to partition a Knowledge Graph into subgraphs to model the various types of attribute triples efficiently. |
| Outcome: | The proposed method achieves significant improvements over 12 baselines in cross-lingual and monolingual datasets. |
Chinese Toxic Language Mitigation via Sentiment Polarity Consistent Rewrites (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) can be effective at rewriting toxic content, but they often default to overly polite rewrites, distorting the emotional tone and communicative intent. |
| Approach: | They evaluate 17 large language models with variant architectures to evaluate their ability to rewrite toxic content while preserving the speaker's original intent. |
| Outcome: | The first Chinese detoxification dataset explicitly designed to preserve sentiment polarity is evaluated across five real-world scenarios. |
LearnerCoMPASS: Intelligent Tutoring System with Dynamic Cognitive Diagnosis and Multi-Model Path Planning (2026.acl-long)
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| Challenge: | Existing adaptive learning systems struggle to achieve deep personalization, dynamic adaptability, and content trustworthiness. |
| Approach: | They propose a framework that integrates large language models into adaptive learning systems . they propose 'cognitive multi-model planning adapted system' to enable deep personalization . |
| Outcome: | The proposed framework outperforms state-of-the-art learning paths and improves trustworthiness. |
How Far are We from Robust Long Abstractive Summarization? (2022.emnlp-main)
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| Challenge: | Abstractive summarization has made tremendous progress in recent years . however, even under a short document setting, abstractive models often generate summaries that are repetitive, ungrammatical, and factually inconsistent with the source. |
| Approach: | They perform fine-grained human annotations to evaluate long document abstractive summarization systems and develop factual consistency metrics. |
| Outcome: | The proposed model can generate more relevant summaries but not factual ones. |
Error Analysis of Uyghur Name Tagging: Language-specific Techniques and Remaining Challenges (L18-1)
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| Challenge: | despite efforts at name tagging, there is limited understanding on the performance ceiling . despite the high-resource language, there are very few natural language processing tools available . |
| Approach: | They propose to use a machine learning model to identify Uyghur name tagger errors . they conclude that such a model is unlikely to be effective for Uygur, or low-resource languages . |
| Outcome: | The proposed model is unlikely to be effective for Uyghur, or low-resource languages in general, the authors argue . they show that the proposed model can be used for high-res languages with superficial features . |
Superficial Self-Improved Reasoners Benefit from Model Merging (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) rely heavily on large-scale reasoning data, but as data becomes scarce, model self-improvement offers a promising alternative. |
| Approach: | They propose to merge the weights of original and self-improved LLMs to mitigate model collapse and improve generalized reasoning capability. |
| Outcome: | The proposed model merge mitigates model collapse and improves generalized reasoning capability. |
IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation (2026.acl-long)
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Yinghao Tang, Xueding Liu, Boyuan Zhang, Tingfeng Lan, Yupeng Xie, Jiale Lao, Yiyao Wang, Haoxuan Li, Tingting Gao, Bo Pan, Luoxuan Weng, Xiuqi Huang, Minfeng Zhu, Yingchaojie Feng, Yuyu Luo, Wei Chen
| Challenge: | Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content. |
| Approach: | They propose to evaluate reliability of text-to-infographic generation using IGenBench . they employ multimodal large language models to verify each question . |
| Outcome: | The proposed framework decomposes reliability verification into atomic yes/no questions based on a taxonomy of 10 question types. |
Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding (2021.emnlp-main)
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| Challenge: | Existing methods to learn effective alignment between vision and language features are insufficient in practice due to complicated multi-step reasoning. |
| Approach: | They propose an iterative alignment network which iterates inter- and intra-modal features within multiple steps for more accurate grounding. |
| Outcome: | The proposed model performs better than the state-of-the-arts on three challenging benchmarks. |
MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows (2025.findings-naacl)
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Xingjian Zhang, Yutong Xie, Jin Huang, Jinge Ma, Zhaoying Pan, Qijia Liu, Ziyang Xiong, Tolga Ergen, Dongsub Shim, Honglak Lee, Qiaozhu Mei
| Challenge: | Scientific innovation is driven by detailed workflows, which include critical steps such as contextualizing literature, generating ideas, validating ideas, and planning new research. |
| Approach: | They propose to use large language models to extract five key aspects from scientific publications to optimize scientific workflows. |
| Outcome: | The proposed dataset includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years. |
LegalChainReasoner: Grounding Criminal Judicial Opinion Generation via Structured Legal Chains (2026.acl-long)
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| Challenge: | Current legalAI tasks divide sentencing and legal reasoning into two separate tasks, resulting in inconsistency between the reasoning and predictions. |
| Approach: | They propose a new task that generates both legal reasoning and sentencing decisions using a framework that applies structured legal chains to guide the model through comprehensive case assessments. |
| Outcome: | The proposed model outperforms baseline models on real-world, open-source Chinese legal case datasets. |
Streaming Hallucination Detection in Long Chain-of-Thought Reasoning (2026.findings-acl)
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Haolang Lu, Minghui Pan, Ripeng LI, Guoshun Nan, Jialin Zhuang, Zijie Zhao, Zhongxiang Sun, Kun Wang, Yang Liu
| Challenge: | Long chain-of-thought reasoning improves performance of large language models, yet hallucinations in such settings often emerge subtly and propagate across reasoning steps. |
| Approach: | They propose to treat step-level hallucination judgments as local observations and introduce a cumulative prefix-level signal that tracks the global evolution of the reasoning state over the entire trajectory. |
| Outcome: | The proposed method enables streaming hallucination detection in long CoT reasoning, providing real-time, interpretable evidence. |
OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)
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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu
| Challenge: | Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024). |
| Approach: | They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers. |
| Outcome: | OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. |
Leveraging Information Bottleneck for Scientific Document Summarization (2021.findings-emnlp)
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| Challenge: | Existing methods to extract salient sentences from document are unsupervised and rely on graph-based methods for sentence ranking. |
| Approach: | They propose an unsupervised extractive approach to document level summarization based on the Information Bottleneck principle. |
| Outcome: | The proposed framework can be extended to a multi-view framework by different signals. |
Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations (2022.emnlp-main)
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| Challenge: | Existing methods for relation prediction in knowledge graphs (KGs) are limited by the inductive setting because entities in training process are finite. |
| Approach: | They propose a graph convolutional network-based model LogCo with logical reasoning by contrastive representations that extracts subgraphs and relational paths between two entities to supply the entity-independence. |
| Outcome: | The proposed model outperforms existing methods on twelve inductive datasets. |
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection (2026.acl-long)
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Junjun Pan, Yixin Liu, Rui Miao, Kaize Ding, Yu Zheng, Quoc Viet Hung Nguyen, Alan Wee-Chung Liew, Shirui Pan
| Challenge: | Existing graph anomaly detection methods rely on coarse sentence-level information and overlook fine-grained lexical cues, limiting their reliability and real-world applicability. |
| Approach: | They propose an explainable and fine-grained safeguarding framework for detecting malicious agents in multi-agent systems (MAS) to incorporate both coarse and fine lexical information for anomalous agent identification. |
| Outcome: | Extensive experiments across diverse MAS topologies and attack scenarios demonstrate robust detection performance and strong interpretability of XG-Guard. |
From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement (2025.emnlp-main)
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| Challenge: | Chain-of-Thought reasoning introduces significant inference latency due to its verbosity. |
| Approach: | They propose a framework that leverages token elasticity phenomenon to progressively compress CoTs via multiround refinement. |
| Outcome: | The proposed method achieves an average accuracy improvement of 5.6% over state-of-the-art baselines while reducing CoT length by an average of 47 tokens and significantly lowering latency. |
Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have similar value rankings but little is known about how susceptible they are to external influence and how different values are correlated with each other. |
| Approach: | They propose to use 6 different value transformation prompting methods to examine the plasticity of LLM value systems by comparing them with 8 LLMs. |
| Outcome: | The proposed methods are effective on 8 LLMs and 3 families. |
Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales (2025.findings-emnlp)
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| Challenge: | Existing work on rationale quality underestimates the importance of CoT distillation, focusing primarily on data quantity, which may result in transferring noisy or incorrect information to the student model. |
| Approach: | They propose a method which can discern and select high quality rationales for distillation and a Rationale Difficulty metric to measure the ability of the student model to generate the correct answer under a given rationale. |
| Outcome: | The proposed method achieves 4.6% accuracy improvement over baseline data on seven datasets over three tasks, controlling accuracy, diversity, and difficulty. |
MarkLLM: An Open-Source Toolkit for LLM Watermarking (2024.emnlp-demo)
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Leyi Pan, Aiwei Liu, Zhiwei He, Zitian Gao, Xuandong Zhao, Yijian Lu, Binglin Zhou, Shuliang Liu, Xuming Hu, Lijie Wen, Irwin King, Philip Yu
| Challenge: | Large Language Models (LLMs) embed imperceptible yet algorithmically detectable signals in outputs to identify LLM-generated text. |
| Approach: | They propose to develop an open-source toolkit for LLM watermarking that embeds imperceptible yet algorithmically detectable signals in model outputs to identify LLM-generated text. |
| Outcome: | MarkLLM provides a unified framework for implementing LLM watermarking algorithms, while providing user-friendly interfaces to ensure ease of access. |
COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)
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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin
| Challenge: | Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. |
| Approach: | They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets. |
| Outcome: | The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark. |
Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration (D19-1)
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| Challenge: | Experimental results show that restoring incomplete utterances from context improves the performance of open-domain dialogue systems. |
| Approach: | They propose to use a dataset to restore incomplete utterances from context . they propose to pick and combine the data to restore the incomplete . |
| Outcome: | The proposed model significantly boosts response quality of open-domain dialogue systems. |
DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI (2026.acl-long)
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| Challenge: | Existing LegalAI tasks are descriptive or predictive, requiring the users to translate the information into legal reasoning. |
| Approach: | They propose a task to generate a structured defence opinion conditioned jointly on an indictment and the defendant’s stated opinion, which often present conflicting claims. |
| Outcome: | The proposed approach improves on eight large language models (LLMs) and shows that it is more efficient than previous approaches. |
Enable Fast Sampling for Seq2Seq Text Diffusion (2024.findings-emnlp)
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| Challenge: | Existing text generation methods use autoregressive (AR) methods, which generate tokens one by one, but are time-consuming. |
| Approach: | They propose an efficient model FMSeq which utilizes flow matching to straighten the generation path, thereby enabling fast sampling for diffusion-based seq2seq text generation. |
| Outcome: | The proposed model generates comparable quality to the SOTA diffusion-based DiffuSeq in just 10 steps, achieving a 200-fold speedup. |
Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning (2025.emnlp-main)
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| Challenge: | a shortage of medical doctors limits access to timely and reliable healthcare . authors propose a multi-turn LLM-based medical assistant for medical inquiries . |
| Approach: | They propose a multi-turn LLM-based medical assistant that asks patients with patience . they compare it with SOTA one-shot and multi-turned LLMs to evaluate its performance . |
| Outcome: | The proposed medical assistant improves diagnostic accuracy, reduces uncertainty and enhances user experience. |
PersonaAgent: Bridging Memory and Action for Personalized LLM Agents (2026.findings-acl)
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Weizhi Zhang, Xinyang Zhang, Chenwei Zhang, Liangwei Yang, Jingbo Shang, Zhepei Wei, Henry Peng Zou, Zijie Huang, Zhengyang Wang, Yifan Gao, Xiaoman Pan, Lian Xiong, Jingguo Liu, Philip S. Yu, Xian Li
| Challenge: | Existing Large Language Model (LLM) enabled agents lack flexibility to respond to users’ varying needs and preferences. |
| Approach: | They propose a test-time user-preference alignment strategy that optimizes the persona prompt, ensuring real-time preference alignment through textual loss feedback between simulated and ground-truth responses. |
| Outcome: | The proposed framework outperforms baseline methods in real-time and in real applications. |
Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding (2022.findings-emnlp)
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Jiahao Zhu, Daizong Liu, Pan Zhou, Xing Di, Yu Cheng, Song Yang, Wenzheng Xu, Zichuan Xu, Yao Wan, Lichao Sun, Zeyu Xiong
| Challenge: | Existing methods for temporal sentence grounding ignore two crucial issues . 1) Boundary-bias: the video downsampling process may lose these two frames . 2) Reasoning-biases: such incorrect new boundary frames lead to the reasoning bias . |
| Approach: | They propose a siamese sampling mechanism to generate additional contextual frames . they use a reasoning strategy to learn the inter-relationship among these frames a . |
| Outcome: | Extensive experiments demonstrate the effectiveness of a new siamese sampling network on three challenging datasets. |
Sentiment Analysis in the Era of Large Language Models: A Reality Check (2024.findings-naacl)
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| Challenge: | Sentiment analysis (SA) has been a long-standing research area in natural language processing. |
| Approach: | They propose a benchmark to evaluate LLMs' SA abilities and propose 'sentiEval' benchmark to be used for a more comprehensive evaluation. |
| Outcome: | The proposed benchmark outperforms small language models on 26 datasets on 13 tasks and compared them with LLMs trained on domain-specific datasets. |
ODL-TempLLM: Ontology-Guided and Description Logic-Reasoned Temporal Reasoning with LLMs (2026.acl-long)
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| Challenge: | Temporal reasoning is crucial for large language models to understand event concurrency and complex temporal interactions in natural language. |
| Approach: | They propose an ontology-guided and description logic–constrained temporal reasoning paradigm that shifts focus from internal inference to the explicit modeling of temporal structure. |
| Outcome: | The proposed method outperforms state-of-the-art methods by 2.07–31.83 F1 points and 1.00–30.73 EM points, exhibiting strong generalization and highlighting the potential of explicit temporal reasoning. |
Feeling Rules in Language Models: Mapping Norms of Emotional Appropriateness Across Roles, Institutions, and Intensity (2026.acl-long)
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| Challenge: | Existing benchmarks measure whether Large language models recognize emotions . authors: LLMs can be used to validate, but they can still judge anger inappropriately . |
| Approach: | They propose a benchmark to measure whether Large language models validate anger . they use explicit norm judgments and implicit acceptability tests to measure norms . |
| Outcome: | The study finds that large differences in sanctioning thresholds and institutional norm signatures are not reducible to overall strictness. |
Towards Robust Temporal Activity Localization Learning with Noisy Labels (2024.lrec-main)
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Daizong Liu, Xiaoye Qu, Xiang Fang, Jianfeng Dong, Pan Zhou, Guoshun Nan, Keke Tang, Wanlong Fang, Yu Cheng
| Challenge: | Existing methods for temporal activity localization are expensive and difficult to satisfy due to subjective labeling. |
| Approach: | They propose a new TAL setting where a TAL model should be robust to mixed training data with noisy moment boundaries. |
| Outcome: | The proposed method is significantly more robust to noisy training data than existing methods. |
DebugBench: Evaluating Debugging Capability of Large Language Models (2024.findings-acl)
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Runchu Tian, Yining Ye, Yujia Qin, Xin Cong, Yankai Lin, Yinxu Pan, Yesai Wu, Hui Haotian, Liu Weichuan, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language models (LLMs) have demonstrated exceptional coding capabilities, but their debugging capabilities remain relatively unexplored. |
| Approach: | They propose a debugging benchmark consisting of 4,253 LLMs with four major bug categories and 18 minor types in C++, Java, and Python. |
| Outcome: | The proposed benchmark covers four major bug categories and 18 minor types in C++, Java, and Python. |
SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables (2023.emnlp-main)
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| Challenge: | Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases arising from crowd-sourced claims and an over-reliance on text-based evidence. |
| Approach: | They present a dataset of 1.2K expert-verified scientific claims that require compositional reasoning for verification. |
| Outcome: | The proposed model outperforms existing models in table-based pretraining models and large language models. |
Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges (2025.findings-acl)
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Hongru Wang, Wenyu Huang, Yufei Wang, Yuanhao Xi, Jianqiao Lu, Huan Zhang, Nan Hu, Zeming Liu, Jeff Z. Pan, Kam-Fai Wong
| Challenge: | Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use focus on stateless, single-turn interactions or partial evaluations, overlooking the inherent stateful nature of interactions in multi-turn applications. |
| Approach: | They propose a multi-turn dialogue dataset with stateful tool interactions considering the whole life cycle of tool use across six key tasks in three stages . they also build VirtualMobile – an embodied virtual mobile evaluation environment to simulate API calls and assess the robustness of the created APIs. |
| Outcome: | The proposed dataset evaluates 13 open- and closed-source LLMs and provides detailed analysis at each stage. |
COSMIC: Generalized Refusal Direction Identification in LLM Activations (2025.findings-acl)
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| Challenge: | Existing methods depend on predefined refusal templates detectable in output tokens or manual review. |
| Approach: | They propose a framework that optimally identifies steering directions and target layers using cosine similarity, entirely independent of output text. |
| Outcome: | The proposed framework achieves comparable steering effectiveness without any prior knowledge or assumptions of a model’s refusal behavior such as the use of certain refusal tokens. |
Beyond Itinerary Planning—A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks (2026.acl-long)
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| Challenge: | Existing studies on LLM performance on travel planning have shown that existing settings are limited due to limited domain coverage, insufficient modeling of users’ implicit preferences in multi-turn conversations, and a lack of evaluation of agents’ capability boundaries. |
| Approach: | They propose a benchmark to evaluate LLMs' planning and tool-use abilities in real-world settings by collecting user queries, user preferences, and tools from real scenarios. |
| Outcome: | The proposed benchmark evaluates agents' capabilities in real-world settings and shows that even advanced models exhibit imbalanced performance across different capabilities. |
Faithful Logical Reasoning via Symbolic Chain-of-Thought (2024.acl-long)
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| Challenge: | SymbCoT is a framework that integrates symbolic expressions and logic rules with CoT prompting. |
| Approach: | They propose a Symbolic Chain-of-Thought framework that integrates symbolic expressions and logic rules with CoT prompting. |
| Outcome: | The proposed framework improves on 5 standard datasets with symbolic expressions and rules . it shows that it is more faithful, flexible, and explainable than the current method . |
Anchoring the Cache: Mitigating Contextual Hallucination in KV-Compressed Long-Context Summarization (2026.acl-long)
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| Challenge: | Recent studies show that KV cache compression can increase hallucination scores in LLMs . modern LLM models support extremely long sequences, but their impact on model hallucinosity remains underexplored. |
| Approach: | They propose a decoding-phase strategy that selectively removes generated KV pairs from retrieval heads responsible for retrieving critical information from source context. |
| Outcome: | The proposed method reduces hallucination across multiple models and datasets while preserving computational efficiency. |
SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning (2026.acl-long)
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Peidong Wang, Zhiming Ma, Xin Dai, YongKang Liu, Shi Feng, Xiaocui Yang, Wenxing Hu, Zhihao Wang, Mingjun Pan, Li Yuan, Daling Wang
| Challenge: | Existing methods for fraud detection rely on transcribed text, lacking acoustic cues . a proposed framework for audio-based slow-thinking fraud detection eliminates transcription errors . |
| Approach: | They propose a framework for audio-based slow-thinking fraud detection that eliminates transcription errors and rewards slow-thought reasoning by capturing fine-grained audio details. |
| Outcome: | The proposed method improves accuracy, inference efficiency, and real-time processing capabilities. |
Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos (2021.emnlp-main)
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| Challenge: | Temporal sentence localization in videos is an important yet challenging task in natural language processing. |
| Approach: | They propose an Adaptive Proposal Generation Network to maintain the segment-level interaction while speeding up the efficiency. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three challenging benchmarks. |
Prompt Refinement with Image Pivot for Text-to-Image Generation (2024.acl-long)
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| Challenge: | Recent advances in text-to-image generation have markedly expanded the boundaries of digital artistry, enabling the creation of visually compelling images with unprecedented ease. |
| Approach: | They propose to decompose the prompt refinement process into two tasks: inferring user-preferred images from user languages and translating them into system languages. |
| Outcome: | Experiments show that PRIP outperforms baselines and transfers to unseen systems in a zero-shot manner. |
Reasoning Step-by-Step: Temporal Sentence Localization in Videos via Deep Rectification-Modulation Network (2020.coling-main)
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| Challenge: | Existing methods for temporal sentence localization in videos focus on visual content, but they are insufficient to model complex video contents. |
| Approach: | They propose a deep rectification-modulation network to correct attention misalignment . they use sentence information to capture frame-to-frame relation . |
| Outcome: | The proposed method achieves state-of-the-art performance on three public datasets. |
HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation (2025.acl-long)
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| Challenge: | Current approaches to addressing knowledge outdating in LLMs struggle with retrieval and generation aspects when handling outdated information. |
| Approach: | They propose a benchmark to evaluate the impact of outdated information on RAG . they use token-level diff algorithms and LLM pipelines to create a large-scale QA dataset . |
| Outcome: | The proposed benchmark analyzes the impact of outdated information on RAG performance. |
Rescorla-Wagner Steering of LLMs for Undesired Behaviors over Disproportionate Inappropriate Context (2025.emnlp-main)
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Rushi Wang, Jiateng Liu, Cheng Qian, Yifan Shen, Yanzhou Pan, Zhaozhuo Xu, Ahmed Abbasi, Heng Ji, Denghui Zhang
| Challenge: | Incorporating external context can enhance the response quality of Large Language Models (LLMs). however, real-world contexts often mix relevant information with disproportionate inappropriate content. |
| Approach: | They propose a Poisoned Context Testbed to pair queries with real-world contexts . they propose 'rw-Steering' to internalize inappropriate signals . |
| Outcome: | The proposed model improves response quality by 39.8% and reverses undesirable behavior curve. |
Bio-RFX: Refining Biomedical Extraction via Advanced Relation Classification and Structural Constraints (2024.emnlp-main)
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| Challenge: | Existing methods for extracting structured data from unstructured texts neglect unique features of the biomedical literature, such as ambiguous entities and nested proper nouns. |
| Approach: | They propose a model that leverages sentence-level relation classification before entity extraction to tackle entity ambiguity. |
| Outcome: | The proposed model outperforms baselines in both NER and RE tasks and has competitive performance compared to the state-of-the-art fine-tuned baselines for RE. |
LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence (2026.findings-acl)
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Wenjin Liu, Haoran Luo, Xin Feng, Xiang Ji, Lijuan Zhou, Rui Mao, Jiapu Wang, Shirui Pan, Erik Cambria
| Challenge: | Existing benchmarks for legal general intelligence (GI) are result-oriented and do not evaluate the legal intelligence of large language models (LLMs). |
| Approach: | They propose a Chinese legal benchmark for evaluating legal GI in large language models . they use recent legal cases and exam questions to create multiple-choice questions . |
| Outcome: | The proposed benchmarks lack a systematic evaluation of the legal intelligence of large language models (LLMs) the results show that even the best LLMs lagging behind human legal professionals. |
Rank-Aware Negative Training for Semi-Supervised Text Classification (2023.tacl-1)
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| Challenge: | Semi-supervised text classification-based paradigms employ the spirit of self-training, but the accuracy of pseudo-labels can be a problem in real-world scenarios. |
| Approach: | They propose a Rank-aware Negative Training framework to address SSTC in noisy label learning . they rank unlabeled texts based on evidential support from the labeled texts. |
| Outcome: | The proposed framework overcomes state-of-the-art alternatives and achieves competitive performance in other scenarios. |
Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning (2025.emnlp-main)
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| Challenge: | Existing knowledge graph completion methods ignore inconsistent representation spaces between natural language and graph structures, leading to duplicate works and time-consuming processes. |
| Approach: | They propose a framework that enhances LLMs for KGC via structure-aware alignment-tuning to align graph embeddings with the natural language space through multi-task contrastive learning. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two KGC tasks across four benchmark datasets. |
Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data (2024.findings-acl)
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| Challenge: | Quantitative reasoning with data is a critical skill to analyze data, yet the assessment of such ability remains limited. |
| Approach: | They propose a quantitative reasoning with data benchmark to evaluate Large Language Models' ability in statistical and causal reasoning with real-world data. |
| Outcome: | The proposed model GPT-4 achieves an accuracy of 58%, while open-source model Deepseek-coder-instruct gets the highest accuracy of 37%. |
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)
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Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains. |
| Approach: | They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries. |
| Outcome: | The proposed system outperforms baselines in the open domain task-solving benchmark. |
GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation (2025.findings-acl)
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Jie He, Jennifer Neville, Mengting Wan, Longqi Yang, Hui Liu, Xiaofeng Xu, Xia Song, Jeff Z. Pan, Pei Zhou
| Challenge: | Large Language Models (LLMs) can expand their capabilities by integrating external tools. |
| Approach: | They propose a training framework that prepares LLMs for diverse generalization challenges in tool utilization. |
| Outcome: | The proposed framework improves the tool-usage capabilities of LLMs by up to 8B parameters, surpassing GPT-4o. |
Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems (2025.emnlp-main)
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| Challenge: | Empirical studies for communication topology design often overlook why and when sparse and dense topologies help or hinder collaboration. |
| Approach: | They propose a topology design approach that balances error suppression and beneficial information propagation by fusing connectivity patterns from dense and sparse graphs. |
| Outcome: | The proposed topology design achieves superior performance across tasks with sparse and dense graphs. |
Synapse: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation (2026.findings-acl)
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Hanqi Jiang, Junhao Chen, Yi Pan, Ling Chen, Weihang You, Yifan Zhou, Ruidong Zhang, Yohannes Abate, Tianming Liu
| Challenge: | Large Language Models excel at generalized reasoning, but lack the ability to accumulate experiences and maintain narrative coherence over long horizons. |
| Approach: | They propose a unified memory architecture that transcends static vector similarity. |
| Outcome: | The proposed model outperforms state-of-the-art methods in temporal and multihop reasoning tasks. |
ISACL: Internal State Analyzer for Copyrighted Training Data Leakage (2025.findings-emnlp)
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| Challenge: | Traditional methods address leaks only after content is generated, which can lead to the exposure of sensitive information. |
| Approach: | They propose a proactive approach: examining LLMs’ internal states before text generation to detect potential leaks. |
| Outcome: | The proposed framework ensures adherence to copyright and licensing requirements while enhancing data privacy and ethical standards. |
Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation (2025.emnlp-main)
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| Challenge: | Intrusion detection systems (IDS) are limited in labeled samples due to scarcity and lack of diversity in malicious samples. |
| Approach: | They propose a semi-supervised framework that integrates Generative Adversarial Networks with Large Language Models to enhance malicious code generation and SQL Injection detection capabilities. |
| Outcome: | The proposed framework enhances malicious code generation and detection capabilities in few-sample learning scenarios. |