Papers by Pengfei Cao
Agent-RewardBench: Towards a Unified Benchmark for Reward Modeling across Perception, Planning, and Safety in Real-World Multimodal Agents (2025.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) are developing but lack external feedback . there is no clear on how to select reward models for agents . |
| Approach: | They propose a benchmark to evaluate agent reward modeling ability in MLLMs . they use multiple dimensions and real-world agent scenarios evaluation . |
| Outcome: | The proposed benchmark evaluates agent performance in multimodal large language models . it covers perception, planning, and safety with 7 scenarios and is highly difficult and high-quality . |
Event Ontology Completion with Hierarchical Structure Evolution Networks (2023.emnlp-main)
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| Challenge: | Existing methods for event detection require predefined schemas, but manual defining is expensive and labor-intensive. |
| Approach: | They propose a task to achieve event clustering, hierarchy expansion and type naming . they propose 'neighbor Contrastive Clustering' module and a Hierarchy-Aware Linking module . |
| Outcome: | The proposed method outperforms baseline methods on three datasets. |
Beyond Under-Alignment: Atomic Preference Enhanced Factuality Tuning for Large Language Models (2025.findings-naacl)
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| Challenge: | Existing work evaluates the factuality of large language models on in-domain (ID) datasets and the factuality on out-of-domain datasets. |
| Approach: | They propose a framework that enhances model’s awareness of factuality at the granularity of individual facts and propose 'Atomic Preference Enhanced Factuality Tuning' this framework enhances the model’ s awareness and accuracy of factual information at the level of individual factual facts. |
| Outcome: | The proposed framework improves model performance by an average of on ID and OOD datasets, which is highly effective. |
Continual Few-shot Event Detection via Hierarchical Augmentation Networks (2024.lrec-main)
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| Challenge: | Existing methods for continual few-shot event detection use labeled data, but in real-world applications, new event types emerge continually. |
| Approach: | They propose a memory-based framework for continual few-shot event detection . they incorporate prototypical augmentation into the memory set to memorize previous event types . |
| Outcome: | The proposed method outperforms existing methods in multiple continual few-shot event detection tasks. |
Enhancing Self-Attention with Knowledge-Assisted Attention Maps (2022.naacl-main)
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Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Defu Cao, Mingliang Zhang1, Yunhai Tong, Yaming Yang, Jing Bai, Ruofei Zhang, Hao Sun, Wei Shen
| Challenge: | Existing works of knowledge infusion depend on multi-task learning frameworks, which are inefficient and require large-scale retraining when new knowledge is considered. |
| Approach: | They propose a method which integrates knowledge-generated attention maps into the self-attention mechanism and integrates it into the model. |
| Outcome: | The proposed model outperforms existing methods on academic datasets and industry-scale ad relevance applications. |
LEGO: A Multi-agent Collaborative Framework with Role-playing and Iterative Feedback for Causality Explanation Generation (2023.findings-emnlp)
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| Challenge: | Causality explanation generation is a generative task that aims to explain why a given cause-effect pair is true using natural language. |
| Approach: | They propose a multi-agent framework with role-playing and iterative feedback for causality explanation generation. |
| Outcome: | The proposed framework is superior to existing frameworks on WIKIWHY and e-CARE datasets. |
Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do (2026.acl-long)
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| Challenge: | Existing open-source models often yield only marginal overall improvements, possibly due to an overemphasis on mathematical reasoning at the expense of broader capabilities. |
| Approach: | They evaluate 12 multimodal tasks using 14 non-reasoning models and 8 reasoning models. |
| Outcome: | The proposed method is effective in multimodal reasoning tasks, the authors show . they show that it lacks the ability to maintain deep visual introspection throughout the reasoning process. |
WilKE: Wise-Layer Knowledge Editor for Lifelong Knowledge Editing (2024.findings-acl)
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| Challenge: | Existing knowledge editing methods focus on single editing, failing to meet the requirements for lifelong editing. |
| Approach: | They propose an approach that selects editing layer based on the pattern matching degree of editing knowledge across different layers in language models. |
| Outcome: | The proposed method improves on GPT2-XL and GPT-J in lifelong editing compared to state-of-the-art methods . |
Tug-of-War between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models (2024.lrec-main)
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| Challenge: | Existing knowledge conflicts in RALMs can ensnare them in a tug-of-war between knowledge and evidence, limiting their practical applicability. |
| Approach: | They propose a method called Conflict-Disentangle Contrastive Decoding (CD2) to better calibrate the model’s confidence. |
| Outcome: | The proposed method can resolve knowledge conflicts in large language models with the help of conflict-disentangle contrast decoding (CD2) . |
Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification (2021.emnlp-main)
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| Challenge: | Existing studies focus on identifying event factuality at sentence level, which leads to conflicts between different mentions of the same event. |
| Approach: | They propose a document-level event factuality identification model that uses local uncertainty and global structure to model event factuality. |
| Outcome: | The proposed method outperforms existing models on two widely used datasets. |
CogKTR: A Knowledge-Enhanced Text Representation Toolkit for Natural Language Understanding (2022.emnlp-demos)
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Zhuoran Jin, Tianyi Men, Hongbang Yuan, Yuyang Zhou, Pengfei Cao, Yubo Chen, Zhipeng Xue, Kang Liu, Jun Zhao
| Challenge: | Existing knowledge-enhanced methods are limited to knowledge-intensive tasks. |
| Approach: | They propose a knowledge-enhanced text representation toolkit for natural language understanding . it combines knowledge acquisition, knowledge representation, knowledge injection and knowledge application . |
| Outcome: | The proposed toolkit supports knowledge acquisition, knowledge representation, knowledge injection, and knowledge application. |
Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models (2025.acl-demo)
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Jiaxiang Liu, Boxuan Xing, Chenhao Yuan, ChenxiangZhang ChenxiangZhang, Di Wu, Xiusheng Huang, Haida Yu, Chuhan Lang, Pengfei Cao, Jun Zhao, Kang Liu
| Challenge: | Existing interpretation methods only support tasks with specific inputs, limiting their practical applications. |
| Approach: | They propose an extensible module that matches different input data with interpretation methods and consolidates the interpreting outputs. |
| Outcome: | The proposed module can match different input data with interpretation methods and consolidate the interpreting outputs. |
A Troublemaker with Contagious Jailbreak Makes Chaos in Honest Towns (2025.acl-long)
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| Challenge: | Existing research focuses on single-agent attacks and shared memory attacks, but real-world scenarios often involve independent memory. |
| Approach: | They propose a large-scale, multi-agent, multitopology attack evaluation framework that exploits the memory of an agent to make it more vulnerable to jailbreak attacks. |
| Outcome: | The proposed framework improves on the troublemaker makes chaos in Honest Town task with 23.51%, 18.95%, and 52.93% improvements in line, star topologies, and 100-agent settings. |
Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models (2024.findings-acl)
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Zhuoran Jin, Pengfei Cao, Hongbang Yuan, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao
| Challenge: | Existing methods to expand internal memory boundaries of language models by providing external context can often conflict, leading to knowledge conflicts. |
| Approach: | They propose a method that prunes conflicting attention heads without updating model parameters. |
| Outcome: | The proposed method can flexibly control eight LMs to use internal memory or external context without updating model parameters. |
Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning (2024.acl-long)
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| Challenge: | Large language models exhibit high-level commonsense reasoning abilities, especially with enhancement methods like Chain-of-Thought (CoT). |
| Approach: | They propose a chain-of-thought-like method to elicit models' potential abilities to generate rationales and answers that are based on attribution tracing and causal tracers to probe the internal working mechanism of the LLM. |
| Outcome: | The proposed method eliminates Toxic CoT problems and improves the model’s overall commonsense reasoning performance by 5.5%. |
MULFE: A Multi-Level Benchmark for Free Text Model Editing (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have impressive capabilities in comprehending human language and vast parametric knowledge obtained from large corpora. |
| Approach: | They propose a multi-level benchmark for free text model editing to bridge the gap . they categorize probe queries into three levels of generalization . |
| Outcome: | The proposed method improves the generalization performance of large langugae models. |
Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations (2026.acl-long)
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| Challenge: | Large language models (LLMs) have impressive capabilities in utilizing external tools, but in practice, they are often exposed to tools that are irrelevant to the user’s query, in which case the desired behavior is to refrain from invocations. |
| Approach: | They propose a new dataset that decouples structural alignment from semantic relevance and propose rebalancing strategies that effectively mitigates structural alignment bias. |
| Outcome: | The proposed approach effectively mitigates structural alignment bias without degrading general tool-use capabilities. |
HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding (2020.acl-main)
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| Challenge: | Existing methods for ICD coding ignore Code Hierarchy and Code Co-occurrence . cost of manual coding estimated to be $25 billion per year in the US . |
| Approach: | They propose a hyperbolic representation method to leverage the code hierarchy and a graph convolutional network to utilize the code co-occurrence. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two widely used datasets. |
Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models generate hallucinated text when confronted with false premise questions . authors propose a method to mitigate false premises hallucinosity . |
| Approach: | They propose a method to constrain false premise attention heads during the model inference process. |
| Outcome: | The proposed method improves performance by constraining false premise attention heads . it yields a notable increase of nearly 20% of model performance . |
LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoning (2024.findings-emnlp)
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Jiachun Li, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Jun Zhao
| Challenge: | Large language models (LLMs) often exhibit poor performance on knowledge-intensive tasks, such as commonsense reasoning. |
| Approach: | They propose a method to elicit, filter and integrate knowledge in large language models (LINKED) they propose 'reward model' to filter out noisy knowledge and 'take marginal consistent reasoning module' |
| Outcome: | The proposed method outperforms SOTA baselines on two commonsense reasoning tasks. |
DiffusionSL: Sequence Labeling via Tag Diffusion Process (2023.findings-emnlp)
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| Challenge: | Sequence Labeling (SL) is a long-standing field of natural language processing. |
| Approach: | They propose a framework that utilizes a conditional discrete diffusion model for generating discrete tag data. |
| Outcome: | The proposed framework outperforms gpt-3.5-turbo on multiple benchmark datasets and tasks. |
ZhuJiu: A Multi-dimensional, Multi-faceted Chinese Benchmark for Large Language Models (2023.emnlp-demo)
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Baoli Zhang, Haining Xie, Pengfan Du, Junhao Chen, Pengfei Cao, Yubo Chen, Shengping Liu, Kang Liu, Jun Zhao
| Challenge: | Various types of LLMs have recently been rapidly developing, such as Llama2 and ChatGLM2 . |
| Approach: | They propose a benchmark that comprehensively evaluates LLMs across 7 ability dimensions covering 51 tasks. |
| Outcome: | The proposed benchmarks are comprehensive and systematic, with a high level of accuracy and authority. |
Learning How to Remember: A Meta-Cognitive Management Method for Structured and Transferable Agent Memory (2026.findings-acl)
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| Challenge: | Existing approaches store memory in fixed representations and reuse it at a single or implicit level of abstraction, which limits generalization and often leads to negative transfer when distribution shift. |
| Approach: | They propose a Meta-Cognitive Memory Abstraction method which decouples task execution from memory management by combining a frozen task model with a learned memory copilot. |
| Outcome: | Experiments on ALFWorld, ScienceWorld, and BabyAI show that the proposed method improves performance, out-of-distribution generalization, and cross-task transfer over several baselines. |
Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization (2024.findings-acl)
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| Challenge: | Large language models (LLMs) can improve summary quality by mirroring a human-like iterative process of critique and refinement starting from the initial draft. |
| Approach: | They propose to use Prompt Chaining and Stepwise Prompting to perform iterative refinement . they propose to combine the two methods to produce a more favorable outcome . |
| Outcome: | The proposed methods can improve summary quality by mirroring a human-like iterative process . the results show that the prompt chaining method produces a more favorable outcome . |
ZhuJiu-Knowledge: A Fairer Platform for Evaluating Multiple Knowledge Types in Large Language Models (2024.naacl-demo)
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| Challenge: | evaluating the knowledge of large language models (LLMs) is crucial, and rapid advancement in large language modeling has heightened the importance of model evaluations. |
| Approach: | They propose a fairer benchmark for evaluating multiple knowledge types of LLMs by focusing on commonsense knowledge, world knowledge, and language knowledge. |
| Outcome: | The proposed framework evaluates 14 current mainstream LLMs and provides a detailed discussion and analysis of their results. |
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification (2021.acl-long)
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| Challenge: | Existing methods for event causality identification (ECI) rely on annotated training data. |
| Approach: | They propose a method to augment training data for event causality identification by iteratively generating new examples and classifying event causalities in a dual learning framework. |
| Outcome: | The proposed method outperforms existing methods on EventStoryLine and Causal-TimeBank. |
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks (2021.emnlp-main)
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Qingbin Liu, Pengfei Cao, Cao Liu, Jiansong Chen, Xunliang Cai, Fan Yang, Shizhu He, Kang Liu, Jun Zhao
| Challenge: | Existing offline DST models require a fixed dataset to train . Existing domain-lifelong learning methods are impractical in real-world applications . |
| Approach: | They propose a domain-lifelong learning method to continuously train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains. |
| Outcome: | The proposed method outperforms state-of-the-art lifelong learning methods by 4.25% and 8.27% on the MultiWOZ and the SGD benchmarks. |
Unlocking the Future: Exploring Look-Ahead Planning Mechanistic Interpretability in Large Language Models (2024.emnlp-main)
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| Challenge: | Recent studies have shown that large language models may possess preliminary planning capabilities. |
| Approach: | They examine the look-ahead planning mechanism in large language models from the perspectives of information flow and internal representations. |
| Outcome: | The proposed model can decode the decision from the output of MHSA in the middle layers at the last token. |
Clinical-Coder: Assigning Interpretable ICD-10 Codes to Chinese Clinical Notes (2020.acl-demos)
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| Challenge: | Existing methods of automatic coding prediction have been successful, but the interpretability of predicted codes is a challenge. |
| Approach: | They propose an online system that can predict ICD codes for Chinese clinical notes by using a Dilated Convolutional Attention network with N-gram Matching mechanism. |
| Outcome: | The proposed system is able to provide supporting information in clinical decision making. |
AscendKernelGen: LLM-Driven Kernel Generation for NPUs (2026.findings-acl)
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Xinzi Cao, Jianyang Zhai, Pengfei Li, Zhiheng Hu, Cen Yan, null Mubingxu, Guanghuan Fang, Bin She, Jiayu Li, Yihan Su, Dongyang Tao, Feidiao Yang, Chang-Dong Wang, Yutong Lu, Weicheng Xue, Bin Zhou, Yonghong Tian
| Challenge: | Neural Processing Units (NPUs) are critical for AI infrastructure, but their development remains a bottleneck due to vendor-specific Domain-Specific Languages (DSLs). |
| Approach: | They propose a framework for NPU kernel development that bridges the gap in hardware-specific coding . compiler success on complex Level-2 kernels improves from 0% to 95.5%, they say . |
| Outcome: | The proposed framework bridges the gap in hardware-specific coding, showing a near-zero success rate on complex kernels. |
Towards Better Chain-of-Thought: A Reflection on Effectiveness and Faithfulness (2025.findings-acl)
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| Challenge: | Chain-of-thought (CoT) prompting demonstrates varying performance under different reasoning tasks. |
| Approach: | They propose to recall extra information from the question to enhance CoT generation and evaluate CoTs based on their information gain. |
| Outcome: | The proposed method improves both the faithfulness and effectiveness of CoT and evaluates it based on their information gain. |
Incremental Event Detection via Knowledge Consolidation Networks (2020.emnlp-main)
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| Challenge: | Existing approaches to event detection require a fixed set of pre-defined event types . existing methods cannot handle semantic ambiguity and training data imbalance problems . |
| Approach: | They propose a Knowledge Consolidation Network to address these issues . they propose to use a prototype enhanced retrospection and hierarchical distillation to mitigate the adverse effects of semantic ambiguity and class imbalance. |
| Outcome: | The proposed method outperforms the state-of-the-art model by 19% and 13.4% of whole F1 score on ACE and TAC benchmarks. |
Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning (2024.lrec-main)
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| Challenge: | Existing studies focus on sentence-level ECI with high-resource languages, leaving document-level DECI with low-resourced languages under-explored. |
| Approach: | They propose a Heterogeneous Graph Interaction Model with Multi-granularity Contrastive Transfer Learning for zero-shot cross-lingual ECI. |
| Outcome: | The proposed model outperforms the state-of-the-art model on monolingual and multilingual scenarios by 9.4% and 8.2% of average F1 score. |
Complex Event Schema Induction with Knowledge-Enriched Diffusion Model (2023.findings-emnlp)
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| Challenge: | Existing studies on event schema induction have been hindered by errors and data quality issues. |
| Approach: | They propose a knowledge-enriched discrete diffusion model that distills event scenario knowledge from LLMs. |
| Outcome: | The proposed model achieves outstanding performance across evaluation metrics. |
Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks (2021.acl-long)
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| Challenge: | Existing methods for identifying causal relations of events are limited . Existing approaches cannot handle well the problem, especially in the condition of lacking training data. |
| Approach: | They propose a Latent Structure Induction Network to integrate external structural knowledge into a causality reasoning task. |
| Outcome: | The proposed approach outperforms existing state-of-the-art methods on two widely used datasets. |
Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism (2021.acl-long)
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| Challenge: | Existing methods for ICD coding ignore the long-tail of code frequency or noisy clinical notes. |
| Approach: | They propose to use an interactive shared representation network to model code co-occurrences while focusing on the clinical note's noteworthy part and extract valuable information through a self-distillation learning mechanism to solve the long-tail problem. |
| Outcome: | The proposed model reduces the long-tail of code frequency and noise in clinical notes and extracts valuable information through a self-distillation learning mechanism. |
RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment (2025.findings-acl)
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Zhuoran Jin, Hongbang Yuan, Tianyi Men, Pengfei Cao, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao
| Challenge: | Existing retrieval augmented language models often overlook effective alignment with human preferences. |
| Approach: | They propose a benchmark to evaluate RMs in retrieval augmented language models . they incorporate 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity . |
| Outcome: | The proposed benchmark combines 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity of data sources. |
DTELS: Towards Dynamic Granularity of Timeline Summarization (2025.naacl-long)
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| Challenge: | Existing timeline summarizations lack flexibility to meet diverse granularity needs . a fine-grained timeline showing the technical details is preferred for news topics . |
| Approach: | They propose a new paradigm to construct adaptive timelines based on user instructions or requirements. |
| Outcome: | The proposed timelines are informative and granularly consistent, but they struggle to generate consistent timelines. |
From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization (2026.findings-acl)
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| Challenge: | Existing research on PTQ spans three primary directions. |
| Approach: | They conduct a systematic analysis of post-training quantization failures using PTQ . they show that targeted repair can mitigate Signal Degradation but remains ineffective for Computation Collapse . |
| Outcome: | The proposed method mitigates Signal Degradation but remains ineffective for Computation Collapse. |
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning (2026.acl-long)
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| Challenge: | Multimodal web agents are cost-efficient and privacy-preserving, but suffer from weak planning and limited cross-website generalization. |
| Approach: | They propose a method which autonomously explores environments to discover experiences and utilizes hindsight experience to synthesize strictly aligned, high-level training data. |
| Outcome: | The proposed method outperforms Qwen2.5-VL-32B model on real-world benchmarks and demonstrates that mastering low-level atomic skills does not guarantee high-level planning competence. |
Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models (2026.findings-acl)
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| Challenge: | Existing scaling laws focus on general performance, overlooking crucial fine-grained factors and how quantization differentially impacts diverse knowledge capabilities. |
| Approach: | They propose a framework that unifies model size, bit-width, and fine-grained factors into memorization, application, and reasoning. |
| Outcome: | The proposed framework shows strong fit and cross-architecture consistency on 293 different PTQ configurations. |
InstructoR: Instructing Unsupervised Conversational Dense Retrieval with Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for conversational retrieval only fine-tune on limited supervised data, making it difficult for the retriever to fully grasp the entire conversation. |
| Approach: | They propose a method to instruct unsupervised conversational dense retrieval with large language models (LLMs) they use supervised data to discover the user's query intent from the conversation context . |
| Outcome: | The proposed method can bring significant improvements across various ad-hoc retrievers, surpassing the current state-of-the-art method. |
Alignment Precedes Fusion: Open-Vocabulary Named Entity Recognition as Context-Type Semantic Matching (2023.findings-emnlp)
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| Challenge: | Continual learning and zero-shot learning approaches have not been adopted to scale to novel-emerging types. |
| Approach: | They propose a method to recognize entities in novel types by their textual names or descriptions. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on three challenging OVNER benchmarks by 9.7%, 9.5%, and 1.8% F1-score of novel types. |
Cracking Factual Knowledge: A Comprehensive Analysis of Degenerate Knowledge Neurons in Large Language Models (2025.acl-long)
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| Challenge: | Large language models (LLMs) are believed to store extensive factual knowledge, yet the mechanisms of knowledge storage in LLMs remain largely unexplored. |
| Approach: | They propose that some multi-layer perceptron neurons can store "knowledge". |
| Outcome: | The proposed model can store "knowledge" in multi-layer perceptron neurons, but not redundancy. |
Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing (2025.acl-long)
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| Challenge: | Existing knowledge editing algorithms are prone to generating original knowledge . despite the fact that many models achieve near-perfect performance, superficial editing remains a challenge . |
| Approach: | They propose to use "**superficial editing**" to describe the phenomenon . they investigate the internal mechanisms of the attention module and their corresponding left singular vectors . |
| Outcome: | The proposed method can modify specific knowledge in a pretrained large language model while ensuring that unrelated knowledge remains unaffected. |
Lightweight Haar Wavelet Subband Pruning for LLMs (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have impressive performance but require computational and memory resources. |
| Approach: | They propose a post-training framework that uses a Haar wavelet transform to prune weights. |
| Outcome: | The proposed pruning framework reduces pruning time and computational costs by removing less important weights while preserving model architecture. |
A Good Neighbor, A Found Treasure: Mining Treasured Neighbors for Knowledge Graph Entity Typing (2022.emnlp-main)
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| Challenge: | Existing methods to infer missing types for knowledge graphs only leverage one-hop neighbor information of the central entity, ignoring multi-hop neighbors that can provide valuable clues for inference. |
| Approach: | They propose a method to infer missing types for knowledge graph entities by using neighbor information and co-occurrence relations between types. |
| Outcome: | The proposed method significantly outperforms existing state-of-the-art methods on two widely used datasets. |
Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement (2021.findings-acl)
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| Challenge: | Existing methods for event causality identification (ECI) rely on labeled data, but the scale of annotated datasets is limited. |
| Approach: | They propose a self-supervised framework to learn context-specific causal patterns from external causal statements and adopt a contrastive transfer strategy to incorporate the learned context- specific causal patterns into the target ECI model. |
| Outcome: | The proposed method significantly outperforms existing methods on EventSto-ryLine and Causal-TimeBank (+2.0 and +3.4 points on F1 value respectively). |
AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation (2024.findings-emnlp)
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Zhitao He, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Jiexin Xu, Huaijun Li, Kang Liu, Jun Zhao
| Challenge: | Recent advances in deep learning have significantly impacted the legal domain. |
| Approach: | They propose a multi-agent framework for judicial decision-making that simulates the court trial process . they propose 420 Chinese judgment documents to support their framework and build a large-scale legal knowledge base . |
| Outcome: | The proposed framework outperforms existing methods in various aspects, especially in generating legal articles. |
M2Edit: Locate and Edit Multi-Granularity Knowledge in Multimodal Large Language Model (2025.emnlp-main)
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| Challenge: | Existing knowledge editing methods for MLLMs lack multi-granularity knowledge . existing knowledge editing approaches lack multimodality knowledge and generalize to multimodal data. |
| Approach: | They propose a multimodal knowledge editing method which integrates key knowledge layers within MLLMs and collaboratively edits them. |
| Outcome: | The proposed method improves visual generality performance on knowledge data of different granularities. |
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism (D18-1)
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| Challenge: | Existing methods for named entity recognition (NER) do not exploit word boundary information from CWS or cannot filter the specific information of CWS. |
| Approach: | They propose to exploit task-shared boundary information to make full use of Chinese NER task and Chinese word segmentation (CWS) . |
| Outcome: | The proposed model significantly outperforms other state-of-the-art methods on two widely used datasets. |
Evaluating Personalized Tool-Augmented LLMs from the Perspectives of Personalization and Proactivity (2025.acl-long)
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| Challenge: | Personalized tool utilization is essential for aligning large language models (LLMs) with user preference in interaction scenarios with various tools. |
| Approach: | They propose a key-point-based LLM evaluation method that mitigates biases by manually annotating key points for each test case and providing them to LLM as the reference. |
| Outcome: | The proposed method mitigates biases in the LLM-as-a-judge system by manually annotating key points for each test case and providing them to LLM as the reference. |
Leros: Learning Explicit Reasoning on Synthesized Data for Commonsense Question Answering (2024.lrec-main)
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Chenhao Wang, Pengfei Cao, Jiachun Li, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Li Qiuxia, Jun Zhao
| Challenge: | Recent work shows large language models can generate useful rationales for commonsense question answering (CQA) however, the cost of deployment and further tuning is relatively expensive for the large models. |
| Approach: | They propose a framework that leverages both knowledge graphs and large language models to synthesize rationale-augmented CQA data. |
| Outcome: | The proposed model can generate useful rationales on unseen CQA benchmarks. |
The Knowledge Microscope: Features as Better Analytical Lenses than Neurons (2025.acl-long)
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| Challenge: | Recent studies have shown that features are superior analytical units for understanding factual knowledge in Language Models. |
| Approach: | They propose a feature-based editing method that decomposes neurons into features rather than neurons to understand the mechanisms of factual knowledge in Language Models. |
| Outcome: | The proposed method demonstrates superior performance over neuron-based approaches in erasing privacy-sensitive information from LMs. |