Papers by Huajun Chen

Agentic Knowledgeable Self-awareness (2025.acl-long)

Challenge:	Existing methods for automatic prompt optimization face two challenges: lack of diversity and semantic drift.
Approach:	They propose a framework for automatic prompt optimization that iteratively refines prompts through text gradients and selects the best prompt using perplexity.
Outcome:	The proposed framework outperforms existing prompt optimization methods and manual prompting on commonsense, mathematical, logical, temporal, and semantic reasoning benchmarks.

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Shuofei Qiao, Zhisong Qiu, Baochang Ren, Xiaobin Wang, Xiangyuan Ru, Ningyu Zhang, Xiang Chen, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen

Challenge:	Large Language Models (LLMs) have achieved considerable performance across various agentic planning tasks.
Approach:	They propose a data-centric approach that applies agents with knowledgeable self-awareness like humans to a heuristic situation judgement criterion to mark special tokens on their self-explored trajectories for collecting training data.
Outcome:	The proposed paradigm outperforms baseline models on various tasks with minimal external knowledge.

KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality (2026.acl-long)

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Baochang Ren, Shuofei Qiao, Ningyu Zhang, Da Zheng, Huajun Chen

OceanGPT: A Large Language Model for Ocean Science Tasks (2024.acl-long)

Challenge:	Existing Reinforcement Learning approaches rely on outcome-oriented rewards to reinforce fabricated reasoning paths when the final answer is correct.
Approach:	They propose a framework that integrates factual supervision directly into reasoning . they propose to decompose chain of thought into atomic facts and verify them against ground-truth knowledge .
Outcome:	The proposed framework reduces the Incorrect Rate on SimpleQA by 20.3% while maintaining strong performance on complex reasoning benchmarks.

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Zhen Bi, Ningyu Zhang, Yida Xue, Yixin Ou, Daxiong Ji, Guozhou Zheng, Huajun Chen

IEPile: Unearthing Large Scale Schema-Conditioned Information Extraction Corpus (2024.acl-short)

Challenge:	Recent advances in Large Language Models (LLMs) have transformed the paradigm in ocean science.
Approach:	They propose a framework to automatically obtain large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration.
Outcome:	The proposed framework shows a higher level of knowledge expertise for ocean science tasks and gains preliminary embodied intelligence capabilities in ocean technology.

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Honghao Gui, Lin Yuan, Hongbin Ye, Ningyu Zhang, Mengshu Sun, Lei Liang, Huajun Chen

DET: A Dual-Encoding Transformer for Relational Graph Embedding (2024.lrec-main)

Challenge:	Large Language Models exhibit a significant performance gap in Information Extraction (IE) high-quality instruction data is the vital key for enhancing LLMs' specific capabilities .
Approach:	They propose a bilingual (English and Chinese) IE instruction corpus that contains 0.32B tokens.
Outcome:	The proposed model improves the performance of LLMs for IE with zero-shot generalization.

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Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Qiang Zhang, Huajun Chen

RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models (2025.emnlp-main)

Challenge:	Existing approaches to graph representation only consider the local neighbors, sacrificing the Transformer’s ability to attend to elements at any distance.
Approach:	They propose a dual-encoding Transformer architecture that uses a structural encoder and a semantic encoder to seek for semantically relevant nodes.
Outcome:	The proposed architecture achieves superior performance compared to state-of-the-art attention-based methods on complex relational graphs like KGs and citation networks.

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Zhaoyan Gong, Juan Li, Zhiqiang Liu, Lei Liang, Huajun Chen, Wen Zhang

LightThinker: Thinking Step-by-Step Compression (2025.emnlp-main)

Challenge:	Current temporal knowledge graph question answering methods focus on implicit temporal constraints and lack the capability to handle complex temporal queries.
Approach:	They propose a temporal knowledge graph question answering framework that recursively decomposes questions into sub-problems and employs multi-path answer aggregation to improve fault tolerance.
Outcome:	The proposed framework outperforms existing methods on multiTQ and TimelineKGQA benchmarks.

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Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang

What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations (2026.acl-short)

Challenge:	Recent advances in Large Language Models have demonstrated their remarkable capabilities in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associated with generating lengthy tokens.
Approach:	They propose a method that dynamically compresses verbose thought steps into compact representations and discards original reasoning chains.
Outcome:	The proposed method reduces peak memory usage and inference time while maintaining competitive accuracy.

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Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen

Generative Knowledge Graph Construction: A Review (2022.emnlp-main)

Challenge:	Existing approaches to replicate AI research are limited by insufficient background knowledge and the limitations of retrieval-augmented generation methods.
Approach:	They propose a pluggable, paper-centric knowledge base that integrates code snippets and technical insights extracted from scientific literature into a verifiable, executable representation.
Outcome:	The proposed knowledge base shows significant performance gains on paperBench when integrated into three agent frameworks with two different LLMs.

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Hongbin Ye, Ningyu Zhang, Hui Chen, Huajun Chen

Zero-shot Text Classification via Reinforced Self-training (2020.acl-main)

Challenge:	Knowledge Graphs (KGs) are a form of structured knowledge that rely almost exclusively on human-curated structured or semi-structured data.
Approach:	They propose to use the sequence-to-sequence framework to build knowledge graphs.
Outcome:	The proposed methods have been compared with existing methods and are promising for the future.

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Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng, Feng Wang, Jun Zhang, Huajun Chen

Low-Resource NLP

Challenge:	Existing methods to learn from unlabeled data are difficult for zero-shot text classification tasks.
Approach:	They propose a self-training based method to efficiently leverage unlabeled data.
Outcome:	The proposed method significantly outperforms existing methods in zero-shot text classification tasks on benchmarks and a real-world e-commerce dataset.

MLBiNet: A Cross-Sentence Collective Event Detection Network (2021.acl-long)

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Dongfang Lou, Zhilin Liao, Shumin Deng, Ningyu Zhang, Huajun Chen

LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (2022.coling-1)

Challenge:	Detecting multiple events from natural language text is a challenge because of the following problems: a) Sentence-level contextual representation and document-level information aggregation are not enough to detect event triggers.
Approach:	They propose a multi-layer bidirectional network to capture document-level association of events and semantic information simultaneously.
Outcome:	The proposed approach improves performance over the current state-of-the-art approach.

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Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang

Low-Resource NLP

Challenge:	Existing approaches for Named Entity Recognition (NER) use extensive labeled data for model training, which struggles in low-resource scenarios.
Approach:	They propose a lightweight tuning paradigm for low-resource NER via pluggable prompting . they construct a learnable verbalizer of entity categories without any label-specific classifiers .
Outcome:	The proposed model outperforms baselines and class transfer models in low-resource scenarios.

SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs (2025.findings-emnlp)

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Zhiqiang Liu, Enpei Niu, Yin Hua, Mengshu Sun, Lei Liang, Huajun Chen, Wen Zhang

Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning (2026.acl-long)

Challenge:	Existing evaluations for Structured Knowledge (SK) understanding are non-rigorous and focus on a single type of SK.
Approach:	They propose a structured knowledge understanding benchmark that includes four widely used structured knowledge forms.
Outcome:	The proposed benchmark is based on four widely used structured knowledge forms . it includes a question, an answer, positive knowledge units, and noisy knowledge units .

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Zhaoyan Gong, Zhiqiang Liu, Songze Li, Xiaoke Guo, Yuanxiang Liu, Xinle Deng, Zhizhen Liu, Lei Liang, Huajun Chen, Wen Zhang

Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (2022.findings-naacl)

Challenge:	Existing methods rely on fixed workflows and expensive closed-source APIs, limiting flexibility and scalability.
Approach:	They propose a temporal reasoning agent that trains on difficult questions first . they expand the action space with specialized internal actions alongside external action .
Outcome:	The proposed agent improves 19.8% over baselines on complex questions and multi-tasks.

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Xiang Chen, Ningyu Zhang, Lei Li, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

EventRAG: Enhancing LLM Generation with Event Knowledge Graphs (2025.acl-long)

Challenge:	Existing approaches for named entity recognition and relation extraction suffer from error sensitivity when irrelevant object images are incorporated in texts.
Approach:	They propose a hierarchical visual prefix fusion NeTwork for visual-enhanced entity and relation extraction using pluggable visual prefixed visual features.
Outcome:	The proposed method achieves state-of-the-art on three benchmark datasets.

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Zairun Yang, Yilin Wang, Zhengyan Shi, Yuan Yao, Lei Liang, Keyan Ding, Emine Yilmaz, Huajun Chen, Qiang Zhang

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)

Challenge:	Existing approaches to text generation often neglect event structures that shape real-world narratives.
Approach:	They propose a framework that integrates structured event semantics with iterative retrieval and inference to enhance text generation.
Outcome:	Experiments on UltraDomain and MultiHopRAG show that the proposed framework outperforms baseline RAG systems in generation effectiveness, logical consistency, and multi-hop reasoning accuracy.

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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

Noise-powered Multi-modal Knowledge Graph Representation Framework (2025.coling-main)

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.

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Zhuo Chen, Yin Fang, Yichi Zhang, Lingbing Guo, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen, Wen Zhang

CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs (2025.acl-long)

Challenge:	Current efforts to integrate MMKG with pretraining are scarce.
Approach:	They propose a method that integrates multi-modal entity features into MMKGs using a Transformer-based architecture equipped with modality-level noise masking.
Outcome:	The proposed method achieves SOTA performance across ten datasets.

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Jizhan Fang, Tianhe Lu, Yunzhi Yao, Ziyan Jiang, Xin Xu, Huajun Chen, Ningyu Zhang

Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks (N19-1)

Challenge:	CKnowEdit is the first-ever knowledge editing dataset designed to correct linguistic, factual, and logical errors in Large Language Models.
Approach:	They propose a Chinese knowledge editing dataset to correct linguistic, factual, and logical errors in Large Language Models.
Outcome:	The proposed dataset highlights the challenges that LLMs face in mastering Chinese . CKnowEdit can correct linguistic, factual, and logical errors in Chinese, the authors show .

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Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang, Huajun Chen

Exploring Model Kinship for Merging Large Language Models (2025.findings-emnlp)

Challenge:	Existing distance supervised relation extraction models for long-tail data are inadequate for many applications.
Approach:	They propose to leverage implicit relational knowledge among class labels and learn explicit relational knowing using graph convolution networks.
Outcome:	The proposed approach outperforms baselines for long-tail relations on a large-scale dataset.

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Yedi Hu, Yunzhi Yao, Ningyu Zhang, Huajun Chen, Shumin Deng

Can We Predict Before Executing Machine Learning Agents? (2026.acl-long)

Challenge:	Model merging has become one of the key technologies for enhancing the capabilities and efficiency of Large Language Models.
Approach:	They propose a model merging strategy that incorporates model kinship to improve model performance.
Outcome:	The proposed model merging strategy can yield better performance on benchmark datasets.

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Jingsheng Zheng, Jintian Zhang, Yujie Luo, Yuren Mao, Yunjun Gao, Lun Du, Huajun Chen, Ningyu Zhang

Challenge:	Existing approaches to scientific discovery rely on expensive physical execution . a Generate-Execute-Feedback paradigm is costly and slow .
Approach:	They propose to internalize execution priors to substitute costly runtime checks with instantaneous predictive reasoning, drawing inspiration from World Models.
Outcome:	The proposed framework achieves 61.5% accuracy and robust confidence calibration when primed with a Verified Data Analysis Report.

SAFER: Advancing Safety Alignment via Efficient Ex-Ante Reasoning (2026.findings-acl)

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Kehua Feng, Keyan Ding, Yuhao Wang, Menghan Li, Fanjunduo Wei, Xinda Wang, Huajun Chen

ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering (2026.acl-long)

Challenge:	Existing alignment methods struggle to cover diverse safety scenarios and remain vulnerable to adversarial attacks.
Approach:	They propose a framework for 'Safety' alignment via eFficient' Ex-Ante-R**easoning that instantiates structured Ex-Ance reasoning and embeds predefined safety rules.
Outcome:	The proposed framework enhances safety performance while maintaining usefulness and efficiency.

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Xiaoke Guo, Songze Li, Zhiqiang Liu, Zhaoyan Gong, Yuanxiang Liu, Huajun Chen, Wen Zhang

Knowledge Rumination for Pre-trained Language Models (2023.emnlp-main)

Challenge:	Existing serialization methods fail to capture explicit hierarchies and lack schema flexibility . Existing tree-based approaches suffer from limited semantic adaptability .
Approach:	They propose a method that leverages the global semantic awareness of LLMs to reconstruct tables into Logical Semantic Trees.
Outcome:	The proposed method achieves state-of-the-art (SOTA) performance on complex table benchmarks.

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Yunzhi Yao, Peng Wang, Shengyu Mao, Chuanqi Tan, Fei Huang, Huajun Chen, Ningyu Zhang

Know the Known and the Unknown: Reasonable Answer Generation with Knowledge-Informed Citations (2026.acl-long)

Challenge:	Existing studies have shown that pre-trained language models lack the capacity to handle knowledge-intensive tasks alone.
Approach:	They propose a new paradigm to help pre-trained language models utilize latent knowledge without retrieving it from external corpus.
Outcome:	The proposed paradigm can be applied to pre-trained language models without retrieving external knowledge from the corpus.

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Yichi Zhang, Zhuo Chen, Lingbing Guo, Jun Xu, Mengshu Sun, Zhizhen Liu, Lei Liang, Wen Zhang, Huajun Chen

Natural Language Interfaces

Challenge:	Existing approaches focus on generating multi-level citations linked to specific references, making it verifiable and trustworthy.
Approach:	They propose a new data construction pipeline and a benchmark to improve citation granularity and awareness of unknown information.
Outcome:	The proposed model improves on the existing benchmark and data construction pipeline and provides citation granularity and awareness of unknown information.

Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction (2020.coling-main)

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Haiyang Yu, Ningyu Zhang, Shumin Deng, Hongbin Ye, Wei Zhang, Huajun Chen

What’s Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning (2026.findings-acl)

Challenge:	Existing approaches to supervised relational triple extraction require huge amounts of labeled data.
Approach:	They propose a multi-prototype embedding network model to extract the composition of relational triples from unstructured text.
Outcome:	The proposed method improves the performance of the few-shot relational triple extraction problem.

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Songze Li, Xiaoke Guo, Tianqi Liu, Biao Yi, Zhaoyan Gong, Zhiqiang Liu, Huajun Chen, Wen Zhang

AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning (2024.acl-long)

Challenge:	Existing GUI reasoning methods rely on direct screen-based decision-making, which lacks interpretability and overlooks a comprehensive understanding of UI elements, ultimately leading to task failure.
Approach:	They propose a GUI reasoning paradigm that treats the GUI reasoning task as a cyclic *Screen-UI elements-Action process.
Outcome:	The proposed paradigm achieves state-of-the-art UI understanding performance while yielding superior results in GUI reasoning tasks.

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Shuofei Qiao, Ningyu Zhang, Runnan Fang, Yujie Luo, Wangchunshu Zhou, Yuchen Jiang, Chengfei Lv, Huajun Chen

Question Answering

Challenge:	Existing language agent systems struggle with costly data reliance and need multiple models for multiple functions.
Approach:	They propose an automatic agent learning framework for QA that synthesizes planning trajectories without human intervention.
Outcome:	The proposed framework outperforms existing models on question-answering tasks with a division-of-labor strategy.

Memp: Exploring Agent Procedural Memory (2026.findings-acl)

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Runnan Fang, Yuan Liang, Xiaobin Wang, Jialong Wu, Shuofei Qiao, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

Challenge:	Large Language Models (LLMs) based agents suffer from brittle procedural memory that is manually engineered or entangled in static parameters.
Approach:	They propose a procedural-memory repository that distills past agent trajectories into fine-grained, step-by-step instructions and higher-level, script-like abstractions.
Outcome:	The proposed repository can be used to improve agents' performance on travelplanner and Alfworld.

Detoxifying Large Language Models via Knowledge Editing (2024.acl-long)

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Mengru Wang, Ningyu Zhang, Ziwen Xu, Zekun Xi, Shumin Deng, Yunzhi Yao, Qishen Zhang, Linyi Yang, Jindong Wang, Huajun Chen

Ruleformer: Context-aware Rule Mining over Knowledge Graph (2022.coling-1)

Challenge:	Existing methods to detoxify Large Language Models (LLMs) are limiting, but knowledge editing can be effective.
Approach:	They propose a baseline method to detoxify Large Language Models (LLMs) they propose supervised fine-tuning and reinforcement learning from human feedback (RLHF)
Outcome:	The proposed method reduces toxicity of large language models with one instance of tuning . it reduces the toxicity, while minimizing the toxins, the authors show .

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Zezhong Xu, Peng Ye, Hui Chen, Meng Zhao, Huajun Chen, Wen Zhang

CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners (2025.emnlp-main)

Challenge:	Existing work on rule mining focuses on mining rules, but how to select appropriate rules for completion of different triplets has not been discussed.
Approach:	They propose to take context information into consideration when selecting suitable rules . they devise a transformer-based rule mining approach, Ruleformer .
Outcome:	The proposed model takes context information into consideration, which helps select suitable rules for inference tasks.

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Yunzhi Yao, Jizhan Fang, Jia-Chen Gu, Ningyu Zhang, Shumin Deng, Huajun Chen, Nanyun Peng

OntoED: Low-resource Event Detection with Ontology Embedding (2021.acl-long)

Challenge:	Existing knowledge editing methods fail to generalize updates to multi-hop reasoning tasks . Existing methods only edit single or a few model layers, inadequately integrate updated knowledge into reasoning pathways.
Approach:	They propose a circuit-aware method that enhances the effective integration of updated knowledge in large language models by leveraging curated data samples guided by their analysis.
Outcome:	The proposed method improves accuracy and accuracy of 20% on the MQuAKE dataset while requiring less memory.

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Shumin Deng, Ningyu Zhang, Luoqiu Li, Chen Hui, Tou Huaixiao, Mosha Chen, Fei Huang, Huajun Chen

Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce (2022.findings-emnlp)

Challenge:	Existing methods to ED rely on training instances and ignore correlation of event types.
Approach:	They propose a process of event ontology population linking event instances to pre-defined event types in event ontoology and ontological embedding to address these problems.
Outcome:	The proposed framework can be applied to new unseen event types by establishing linkages to existing ones.

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Yincen Qu, Ningyu Zhang, Hui Chen, Zelin Dai, Chengming Wang, Xiaoyu Wang, Qiang Chen, Huajun Chen

Commonsense Reasoning

Challenge:	Existing models rank statements solely by confidence scores, and there is no information about which ones are salient from a human perspective.
Approach:	They propose a task where a model is required to learn whether a triple is salient . they propose supervised salience evaluation using a new Benchmark dataset .
Outcome:	The proposed task is based on a new Benchmark dataset of salience evaluation in e-commerce . it shows that saliency evaluation is hard, where models perform poorly on evaluation set .

OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking (2025.emnlp-main)

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Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

Information Retrieval

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated remarkable progress in machine writing such as open domain long-form generation.
Approach:	They propose a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection to improve the knowledge density of generated articles.
Outcome:	The proposed framework improves the knowledge density of generated articles without compromising metrics such as coherence and depth.

Reasoning with Language Model Prompting: A Survey (2023.acl-long)

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Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen

ReLearn: Unlearning via Learning for Large Language Models (2025.acl-long)

Challenge:	Reasoning is an essential ability for complex problem-solving and can provide back-end support for various real-world applications.
Approach:	They present cutting-edge research on reasoning with language model prompting and provide systematic resources to help beginners.
Outcome:	The proposed approaches have not been systematically reviewed and analyzed.

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Haoming Xu, Ningyuan Zhao, Liming Yang, Sendong Zhao, Shumin Deng, Mengru Wang, Bryan Hooi, Nay Oo, Huajun Chen, Ningyu Zhang

Editing Conceptual Knowledge for Large Language Models (2024.findings-emnlp)

Challenge:	Existing methods for unlearning large language models often rely on reverse optimization to reduce target token probabilities.
Approach:	They propose a data augmentation and fine-tuning pipeline for effective unlearning . they propose augmentation, evaluation frameworks to measure contextual forgetting .
Outcome:	The proposed framework achieves targeted forgetting while preserving high-quality outputs.

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Xiaohan Wang, Shengyu Mao, Shumin Deng, Yunzhi Yao, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang

Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention (2020.findings-emnlp)

Challenge:	Existing knowledge editing methods can modify concept-level definitions, but they can distort instantial knowledge in LLMs, leading to poor performance.
Approach:	They construct a benchmark dataset ConceptEdit and establish new metrics for evaluation to investigate the editing capability of LLMs.
Outcome:	The proposed methods can modify concept definitions but can distort instantial knowledge in LLMs, leading to poor performance.

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Ningyu Zhang, Shumin Deng, Juan Li, Xi Chen, Wei Zhang, Huajun Chen

Prompt-fused Framework for Inductive Logical Query Answering (2024.lrec-main)

Challenge:	Existing approaches to generate answer summarization for medical questions are not straightforward to apply to the medical domain.
Approach:	They propose an approach that utilizes graph convolution networks and question-focused dual attention for Chinese medical answer summarization.
Outcome:	The proposed model generates more coherent and informative summaries compared with baseline models.

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Zezhong Xu, Wen Zhang, Peng Ye, Lei Liang, Huajun Chen

Can We Edit Multimodal Large Language Models? (2023.emnlp-main)

Challenge:	Existing methods for addressing logical queries on knowledge graphs neglect missing edges in KGs . Existing approaches focus on addressing missing edges, thereby neglecting the emergence of new entities .
Approach:	They propose a query-aware prompt-fused framework that addresses embedding of emerging entities . they propose to use a symbolic query to gather information relevant to the query .
Outcome:	The proposed framework addresses embedding of emerging entities through contextual information aggregation.

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Siyuan Cheng, Bozhong Tian, Qingbin Liu, Xi Chen, Yongheng Wang, Huajun Chen, Ningyu Zhang

Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering (2024.findings-acl)

Challenge:	Existing methods to edit multimodal models have been used to incrementally infuse a language model with a new set of facts.
Approach:	They construct a benchmark for editing multimodal Large Language Models and establish metrics for evaluation.
Outcome:	The proposed benchmarks show that editing multimodal models is not as difficult as editing single-modal models.

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Yichi Zhang, Zhuo Chen, Yin Fang, Yanxi Lu, Li Fangming, Wen Zhang, Huajun Chen

Natural Language Interfaces

Challenge:	Domain-specific question answering (QA) requires a comprehensive understanding of a specific domain to answer specialized questions.
Approach:	They propose a new alignment objective to align the LLM preference with different human preferences uniformly to optimize LLM performance in real-world, domain-specific QA settings.
Outcome:	The proposed pipeline is superior for real-scenario domain-specific question answering with LLMs.

Editing Large Language Models: Problems, Methods, and Opportunities (2023.emnlp-main)

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Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang

Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms (2025.acl-long)

Challenge:	Recent advances in model editing for LLMs have created challenges and opportunities for the community.
Approach:	They propose to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs.
Outcome:	The proposed method alters behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs.

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Mengru Wang, Ziwen Xu, Shengyu Mao, Shumin Deng, Zhaopeng Tu, Huajun Chen, Ningyu Zhang

Structured and Abstractive Reasoning on Multi-modal Relational Knowledge Images (2026.findings-acl)

Challenge:	Recent research has explored the use of sparse autoencoders (SAE) to disentangle knowledge in high-dimensional spaces for steering.
Approach:	They propose a method that isolates and manipulates disentangled knowledge components to enhance safety by using sparse autoencoders to disentangle knowledge in high-dimensional spaces for steering.
Outcome:	The proposed method is able to isolate and manipulate disentangled knowledge components to enhance safety in large reasoning models.

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Yichi Zhang, Zhuo Chen, Lingbing Guo, Wen Zhang, Huajun Chen

Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (2022.findings-emnlp)

Challenge:	Existing studies on understanding and reasoning with abstractive information from the visual modality have not explored the use of STructured and Abstractive Reasoning (STAR) on such data.
Approach:	They propose an automatic STAR data engine to synthesize images with MMRK to build multi-modal instructions with reliable chain-of-thought thinking for various STAR tasks.
Outcome:	The proposed framework outperforms GPT-4o in STAR and improves performance across 8 open-source MLLMs.

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Xin Xu, Xiang Chen, Ningyu Zhang, Xin Xie, Xi Chen, Huajun Chen

Low-Resource NLP

Challenge:	Existing approaches to extract relational facts from text are limited in their ability to learn from limited labeled data.
Approach:	They propose to use prompt-based methods with few-shot labeled data to evaluate performance . data augmentation technologies and self-training are also proposed to generate more labeles in-domain data.
Outcome:	The proposed methods perform well in low-resource settings with 8 relation extraction datasets.

How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training (2025.findings-acl)

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Yixin Ou, Yunzhi Yao, Ningyu Zhang, Hui Jin, Jiacheng Sun, Shumin Deng, Zhenguo Li, Huajun Chen

Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking (2025.acl-long)

Challenge:	Large Language Models (LLMs) have exceptional capabilities in knowledge-intensive tasks . however, they struggle with knowledge updates due to dynamic nature of world knowledge .
Approach:	They propose to identify computational subgraphs that facilitate knowledge storage and processing . they also identify a phase shift from formation to optimization in LLMs .
Outcome:	The proposed model can capture factual knowledge from pre-training corpus and encapsulate it as extensive parametric knowledge.

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Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Shaokai Chen, Mengshu Sun, Binbin Hu, Zhiqiang Zhang, Lei Liang, Wen Zhang, Huajun Chen

Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification (2020.coling-main)

Challenge:	Existing methods to integrate external knowledge into LLMs focus on specific problems, lacking a comprehensive exploration of the generalization and capability boundaries of SKP.
Approach:	They propose a new paradigm for structural knowledge prompting to integrate external structural knowledge into LLMs by incorporating structural representations.
Outcome:	The proposed benchmark SUBARU enables the evaluation of the generalization capabilities of SKP from four perspectives.

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Juan Li, Ruoxu Wang, Ningyu Zhang, Wen Zhang, Fan Yang, Huajun Chen

Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization (2025.acl-long)

Challenge:	Existing methods to zero-shot relation classification can only identify seen relations . existing methods rely on descriptive information to improve understandability of relation types .
Approach:	They propose a logic-guided semantic representation learning model for zero-shot relation classification that builds connections between seen and unseen relations via implicit and explicit semantic representations with knowledge graph embeddings and logic rules.
Outcome:	The proposed model can generalize to unseen relation types and achieve promising improvements.

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Yuhao Wang, Keyan Ding, Kehua Feng, Zeyuan Wang, Ming Qin, Xiaotong Li, Qiang Zhang, Huajun Chen

Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)

Challenge:	Protein language models pose significant risks of generating harmful sequences, e.g., viral transmissibility, drug resistance, environmental imbalances, public health crises, etc.
Approach:	They propose a protein-based model that integrates prior knowledge via a Protein Safety Knowledge Graph to minimize the risk of generating harmful sequences.
Outcome:	The proposed framework reduces the likelihood of producing hazardous sequences while maintaining high functionality.

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Guanying Wang, Wen Zhang, Ruoxu Wang, Yalin Zhou, Xi Chen, Wei Zhang, Hai Zhu, Huajun Chen

CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs (2026.acl-long)

Challenge:	Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities.
Approach:	They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier.
Outcome:	The proposed method performs well in the current distant supervision dataset.

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Yuanxiang Liu, Songze Li, Xiaoke Guo, Zhaoyan Gong, Qifei Zhang, Huajun Chen, Wen Zhang

Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency (2026.acl-long)

Challenge:	Existing approaches to large language models often exhibit cognitive rigidity, causing reasoning stagnation.
Approach:	They propose a training-free framework that mimics the interplay between intuition and deliberation.
Outcome:	The proposed framework outperforms state-of-the-art approaches on three benchmarks.

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Haoming Xu, Ningyuan Zhao, Yunzhi Yao, Weihong Xu, Hongru Wang, Xinle Deng, Shumin Deng, Jeff Z. Pan, Huajun Chen, Ningyu Zhang

Challenge:	Existing evaluations rely on point-wise confidence, which can mask brittle belief.
Approach:	They propose a measure of belief robustness that evaluates coherence across a conceptual neighborhood.
Outcome:	The proposed model is more resistant to interference than existing models.

EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models (2024.acl-demos)

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Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen

Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)

Challenge:	Large Language Models (LLMs) suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data.
Approach:	They propose an easy-to-use knowledge editing framework for Large Language Models that allows users to easily edit updated knowledge and adjust undesired behavior while minimizing the impact on unrelated inputs.
Outcome:	The proposed framework surpasses traditional fine-tuning in terms of reliability and generalization.

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Yin Hua, Zhiqiang Liu, Mingyang Chen, Zheng Fang, Chi Man Wong, Lingxiao Li, Chi Man Vong, Huajun Chen, Wen Zhang

How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities (2026.acl-long)

Challenge:	Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks.
Approach:	They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration.
Outcome:	The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks.

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Ziwen Xu, Kewei Xu, Haoming Xu, Haiwen Hong, Longtao Huang, Hui Xue, Ningyu Zhang, Yongliang Shen, Guozhou Zheng, Huajun Chen, Shumin Deng

Deep Reinforcement Learning for Entity Alignment (2022.findings-acl)

Challenge:	Large language models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors pose significant risks.
Approach:	They propose a hierarchical benchmark for evaluating LLM controllability across three domains: language features, sentiment, and personality.
Outcome:	The proposed framework offers a principled and interpretable framework for safe and controllable LLM behavior serving as a foundation for future research.

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Lingbing Guo, Yuqiang Han, Qiang Zhang, Huajun Chen

Representation Learning

Challenge:	Entity alignment (EA) methods identify the aligned entities based on cosine similarity, ignoring the semantics underlying the embeddings themselves.
Approach:	They propose to model entity alignment as a sequential decision-making task where an agent sequentially decides whether two entities are matched or mismatched based on representation vectors.
Outcome:	The proposed framework consistently advances the performance of several state-of-the-art methods, with a maximum improvement of 31.1% on Hits@1.

Schema-adaptable Knowledge Graph Construction (2023.findings-emnlp)

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Hongbin Ye, Honghao Gui, Xin Xu, Xi Chen, Huajun Chen, Ningyu Zhang

Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition (2025.acl-long)

Challenge:	Existing Knowledge Graph Construction (KGC) tasks rely on static information extraction with a closed set of pre-defined schemas.
Approach:	They propose a static knowledge Graph Construction task that extracts entity, relation, and event based on dynamically changing schema graph without retraining.
Outcome:	The proposed system outperforms existing methods but still has room for improvement . it can extract entity, relation, and event based on dynamically changing schema graph without re-training .

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Kehua Feng, Keyan Ding, Tan Hongzhi, Kede Ma, Zhihua Wang, Shuangquan Guo, Cheng Yuzhou, Ge Sun, Guozhou Zheng, Qiang Zhang, Huajun Chen

OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs (2024.findings-emnlp)

Challenge:	Existing methods for evaluation of large language models are inefficient and inefficient due to inaccuracy of standard metrics in human perception of text quality and inefficiency in sampling informative test examples.
Approach:	They propose a sample-efficient human evaluation method for large language models based on the principle of MAximum Discrepancy (MAD) competition.
Outcome:	The proposed method achieves the “golden” ranking of LLMs with a minimum set of input instructions, which in turn reveal their relative strengths and weaknesses.

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Jintian Zhang, Cheng Peng, Mengshu Sun, Xiang Chen, Lei Liang, Zhiqiang Zhang, Jun Zhou, Huajun Chen, Ningyu Zhang

Green & Sustainable NLP

Challenge:	Recent advances in Large Language Models (LLMs) have significantly enhanced the generative capabilities for various NLP tasks, but they still suffer from hallucinations due to their exclusive reliance on parametric knowledge.
Approach:	They propose a framework that integrates retrieval tokens generated autoregressively into a single LLM to handle both tasks simultaneously in a unified forward pass.
Outcome:	The proposed framework bridges the traditionally separate training approaches for generation and retrieval by incorporating retrieval tokens generated autoregressively.

Boosting LLM’s Molecular Structure Elucidation with Knowledge Enhanced Tree Search Reasoning (2025.acl-long)

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Xiang Zhuang, Bin Wu, Jiyu Cui, Kehua Feng, Xiaotong Li, Huabin Xing, Keyan Ding, Qiang Zhang, Huajun Chen

EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models (2024.acl-demos)

Challenge:	Molecular structure elucidation involves deducing a molecule’s structure from various types of spectral data, which is crucial in chemical experimental analysis.
Approach:	They propose a Knowledge-enhanced reasoning framework for Molecular Structure Elucidation that leverages Monte Carlo Tree Search for test-time scaling as a plugin to extend the LLMs’ coverage of the chemical structure space.
Outcome:	The proposed framework significantly improves on both GPT-4o-mini and GPT4o, and a specialized molecule-spectrum scorer improves performance.

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Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Runnan Fang, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen

LCQMC:A Large-scale Chinese Question Matching Corpus (C18-1)

Challenge:	Large Language Models (LLMs) have improved performance across tasks and domains . instruction tuning is a crucial technique to enhance the capabilities of LLMs - but there is no standard open-source instruction processing framework available for the community .
Approach:	They propose an open-source instruction tuning framework for Large Language Models that modularizes instruction generation, selection, prompting and their combination and interaction.
Outcome:	The proposed framework is open-source and available on Github.

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Xin Liu, Qingcai Chen, Chong Deng, Huajun Zeng, Jing Chen, Dongfang Li, Buzhou Tang

Paraphrasing

Challenge:	Existing methods for question answering system lack large-scale question matching corpora . lack of large-sized question matching results in problem solving .
Approach:	They propose a large-scale Chinese question matching corpus which is released to the public . they use a search engine to collect large-sized question pairs related to high-frequency words .
Outcome:	The proposed corpus is more general than paraphrase corpus as it focuses on intent matching rather than paraphrasing.

Making Language Models Better Tool Learners with Execution Feedback (2024.naacl-long)

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Shuofei Qiao, Honghao Gui, Chengfei Lv, Qianghuai Jia, Huajun Chen, Ningyu Zhang

Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching (2025.emnlp-main)

Challenge:	Existing tool learning methodologies induce large language models to utilize tools indiscriminately . Existing frameworks that teach language models when and how to use tools propagate errors rather than enhance performance.
Approach:	They propose a framework that enables large language models to continually learn through feedback derived from tool execution.
Outcome:	The proposed framework can make large language models selectively use tools . it improves accuracy while enhancing insufficient tool learning, it shows .

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Songze Li, Zhiqiang Liu, Zhengke Gui, Huajun Chen, Wen Zhang

Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs (D19-1)

Challenge:	Existing methods employ resource-intensive, non-scalable workflows reasoning on vanilla KGs, but overlook this gap.
Approach:	They propose a flexible framework that leverages LLMs’ prior knowledge to enrich KGs and bridge the semantic gap between queries and graphs.
Outcome:	The proposed framework bridges the semantic gap between structured knowledge graphs and unstructured queries while ensuring low computational costs, scalability, and adaptability across different methods.

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Mingyang Chen, Wen Zhang, Wei Zhang, Qiang Chen, Huajun Chen

Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion (2024.lrec-main)

Challenge:	Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks.
Approach:	They propose a Meta Relational Learning framework to do few-shot link prediction in KGs by observing only a few associative triples.
Outcome:	The proposed model achieves state-of-the-art results on few-shot link prediction KG benchmarks.

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Yichi Zhang, Zhuo Chen, Lei Liang, Huajun Chen, Wen Zhang

InstructProtein: Aligning Human and Protein Language via Knowledge Instruction (2024.acl-long)

Challenge:	Existing MMKGC methods overlook the imbalance problem of modality information among entities, resulting in inadequate modal fusion and inefficient utilization of the raw modality.
Approach:	They propose to integrate structural, visual, and textual information of entities into the discriminant models to predict the missing triples.
Outcome:	The proposed model outperforms 19 recent methods and achieves state-of-the-art results on three public MMKGC benchmarks.

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Zeyuan Wang, Qiang Zhang, Keyan Ding, Ming Qin, Xiang Zhuang, Xiaotong Li, Huajun Chen

MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules (2026.findings-acl)

Challenge:	Large Language Models (LLMs) are a promising new approach to understanding biological sequences such as proteins.
Approach:	They propose an LLM that can generate protein sequences in human and protein languages by pre-training an Lm on protein and natural language corpora and supervised instruction tuning to facilitate alignment.
Outcome:	The proposed model outperforms state-of-the-art LLMs on protein-text generation tasks by a large margin.

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Tong Xu, Xinzhe Cao, Zhihui Zhu, Keyan Ding, Huajun Chen

Croppable Knowledge Graph Embedding (2025.acl-long)

Challenge:	generative models may produce molecules with toxic, reactive, or otherwise hazardous characteristics.
Approach:	They propose a benchmark to evaluate and analyze the safety risks of molecular generation.
Outcome:	The proposed benchmark aims to evaluate and analyze the safety risks of molecular generation.

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Yushan Zhu, Wen Zhang, Zhiqiang Liu, Mingyang Chen, Lei Liang, Huajun Chen

Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction (D18-1)

Challenge:	Knowledge Graph Embedding (KGE) is a common approach for Knowledge Grasse (KGs) in AI tasks.
Approach:	They propose a new KGE training framework MED that allows one training to obtain a croppable KGE model for multiple scenarios with different dimensional needs.
Outcome:	The proposed framework improves low-dimensional sub-models and makes high-dimensional models retain the low-dimension sub-modells’ capacity.

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Ningyu Zhang, Shumin Deng, Zhanling Sun, Xi Chen, Wei Zhang, Huajun Chen

OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)

Challenge:	Existing neural networks focus on instance representation, and subsampling fails to retain precise spatial relationships between higher-level parts.
Approach:	They propose a neural approach based on capsule networks with attention mechanisms to extract relational information from a capsule.
Outcome:	The proposed method improves the precision of the predicted relations with different benchmarks.

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Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen

Knowledge Mechanisms in Large Language Models: A Survey and Perspective (2024.findings-emnlp)

Challenge:	a large number of natural language processing tasks focus on token-level or sentence-level understandings.
Approach:	They propose an open-source and extensible toolkit for various extraction tasks . they deploy an online demo with restful APIs to support real-time extraction .
Outcome:	The proposed model can be used to extract information from text without training and deployment.

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Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models (2025.emnlp-demos)

Challenge:	Using large language models, we can understand knowledge mechanisms in LLMs for learning, storage, utilization, and evolution.
Approach:	They propose to analyze knowledge mechanisms in Large Language Models (LLMs) they examine utilization, evolution, and the potential dark knowledge (hypothesis) they hope to help understand knowledge in LLMs and provide insights for future research .
Outcome:	The proposed model can be used to analyze the evolution of parametric knowledge in LLMs.

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Ziwen Xu, Shuxun Wang, Kewei Xu, Haoming Xu, Mengru Wang, Xinle Deng, Yunzhi Yao, Guozhou Zheng, Huajun Chen, Ningyu Zhang

RaFe: Ranking Feedback Improves Query Rewriting for RAG (2024.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated extraordinary capabilities, however, they may still generate unreliable or unsafe outputs.
Approach:	They propose a framework that allows plug-and-play adjustability for controlling Large Language Model (LLM) behaviors.
Outcome:	The framework is designed to enable plug-and-play adjustability for controlling Large Language Model (LLM) behaviors.

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Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

Text Generation

Challenge:	Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved to enhance document retrieval by reformulating queries.
Approach:	They propose a framework for training query rewriting models that leverages a reranker framework.
Outcome:	The proposed framework provides ranking feedback aligned well with the rewriting objectives without needing signals from annotations and supports both online and offline training models.

Contrastive Demonstration Tuning for Pre-trained Language Models (2022.findings-emnlp)

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Xiaozhuan Liang, Ningyu Zhang, Siyuan Cheng, Zhenru Zhang, Chuanqi Tan, Huajun Chen

SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement (2025.acl-short)

Challenge:	Recent studies focus on searching discrete or continuous prompts or optimized verbalizers, yet the demonstration examples are crucial for an excellent final performance of prompt-tuning.
Approach:	They propose a pluggable, extensible, and efficient approach to prompt tuning that is free of demonstration sampling.
Outcome:	The proposed approach can be pluggable, extensible, and efficient on 16 datasets.

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Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen

Challenge:	Using Large Language Models (LLMs)-based agents can enhance their understanding of environments and tasks.
Approach:	They propose a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search exploration to refine their action knowledge in the current environment.
Outcome:	The proposed framework synthesizes possible scenarios with multi-step action invocation within the action space and performs Monte Carlo Tree Search exploration to refine action knowledge in the current environment.

Unified Hallucination Detection for Multimodal Large Language Models (2024.acl-long)

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Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang, Xiaoyan Yang, Qiang Li, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen

KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents (2025.findings-naacl)

Challenge:	despite significant strides in multimodal tasks, MLLMs are plagued by the critical issue of hallucination.
Approach:	They propose a meta-evaluation benchmark to facilitate evaluation of advancements in hallucination detection methods.
Outcome:	The proposed framework validates hallucinations robustly and provides strategic insights . MHaluBench is a meta-evaluation benchmark designed to facilitate evaluation .

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Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang

Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics (2026.acl-long)

Challenge:	Large Language Models (LLMs) fail to effectively guide the planning trajectories during task solving and result in planning hallucinations.
Approach:	They propose a novel approach to enhance the planning capabilities of large language models by incorporating explicit action knowledge.
Outcome:	The proposed approach can achieve comparable or superior performance to existing baselines on HotpotQA and ALFWorld.

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Ziwen Xu, Chenyan WU, Hengyu Sun, Haiwen Hong, Mengru Wang, Yunzhi Yao, Longtao Huang, Hui Xue, Shumin Deng, Zhixuan Chu, Huajun Chen, Ningyu Zhang

Enhancing Cross Text-Molecule Learning by Self-Augmentation (2024.findings-acl)

Challenge:	Methods for controlling large language models (LLMs) are often studied in isolation, obscuring connections and making comparison difficult.
Approach:	They propose a preference-utility analysis that separates control effects into preference and utility, and measures both on a shared log-odds scale using polarity-paired contrastive examples.
Outcome:	The proposed approach improves preference while preserving utility.

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Yinuo Jiang, Xiang Zhuang, Keyan Ding, Qiang Zhang, Huajun Chen