Papers by Min Peng
Towards Explainable Temporal Reasoning in Large Language Models: A Structure-Aware Generative Framework (2025.findings-acl)
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| Challenge: | Existing studies on temporal reasoning models neglect the explainable reasoning processes underlying the results. |
| Approach: | They propose a structure-aware generative framework that integrates Graph structures with text for Explainable TEmporal Reasoning. |
| Outcome: | The proposed framework achieves state-of-the-art performance while also demonstrating robust generalization capabilities. |
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction (2022.findings-emnlp)
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| Challenge: | Existing methods for linking knowledge graphs lack contextual information in entity neighborhoods, which leads to false prediction results. |
| Approach: | They propose a Schema-augmented Multi-level contrastive LEarning framework to conduct knowledge graph link prediction using a knowledge graph schema. |
| Outcome: | The proposed framework is based on a knowledge graph schema and is compared against state-of-the-art datasets. |
SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis (2025.coling-main)
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Senbin Zhu, ChenYuan He, Hongde Liu, Pengcheng Dong, Hanjie Zhao, Yuchen Yan, Yuxiang Jia, Hongying Zan, Min Peng
| Challenge: | Currently, most sentiment analysis corpora use sequence-level annotation. |
| Approach: | They propose a two-stage approach to financial entity-level sentiment analysis called Self-aware In-context Learning Correction. |
| Outcome: | The proposed approach achieves state-of-the-art on the largest English and Chinese financial entity-level sentiment analysis datasets to date. |
Inductive Topic Variational Graph Auto-Encoder for Text Classification (2021.naacl-main)
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| Challenge: | Existing methods for text classification do not assume explicit latent semantic structure of documents, making them less effective and difficult to interpret. |
| Approach: | They propose a model that integrates a topic model into variational graph-auto-encoder to capture hidden semantic information between documents and words. |
| Outcome: | The proposed model outperforms existing models on supervised and semi-supervised text classification and unsupervised representation learning. |
Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning (2024.findings-naacl)
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| Challenge: | Existing text-based methods for Temporal Knowledge Graph Reasoning struggle to balance textual knowledge and temporal information with expensive purpose-built training strategies. |
| Approach: | They propose a Contrastive historical modeling framework with prefix-tuning for TEmporal Reasoning that feeds history-contextualized text into the pseudo-Siamese encoders to strike a textual-temporal balance. |
| Outcome: | The proposed framework achieves superior performance on four transductive and three few-shot inductive TKGR benchmarks. |
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications (P19-1)
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| Challenge: | Existing approaches to generalize deep neural networks are datahungry and generalize poorly from small datasets. |
| Approach: | They propose an agreement score to evaluate routing processes at instance-level and an adaptive optimizer to enhance routing. |
| Outcome: | The proposed approach improves on two NLP tasks and in low-resource settings with few training instances. |
Graph Relational Topic Model with Higher-order Graph Attention Auto-encoders (2021.findings-acl)
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| Challenge: | Existing methods for learning low-dimensional representations of networked documents are largely ignored for document networks. |
| Approach: | They propose a graph relational topic model to explore document neighborhood information . the model can learn efficient networked document representations in the latent topic space . |
| Outcome: | The proposed model outperforms existing methods on unsupervised representation learning and other downstream tasks. |
TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice (2026.acl-long)
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| Challenge: | Large Language Models excel in general domains but lack real-world practical capabilities. |
| Approach: | They propose a benchmark for Chinese taxation practice that combines 10 traditional application tasks with 3 pioneering real-world scenarios. |
| Outcome: | The proposed benchmark combines 10 traditional tasks with 3 pioneering real-world scenarios. |
JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Sampling (2025.emnlp-main)
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| Challenge: | Recent advances in Large Language Models have improved Text-to-SQL methods . however, they still face challenges such as complex multi-stage pipelines and poor robustness to noisy schema information. |
| Approach: | They propose a single-stage SFT framework that optimizes schema linking and SQL generation via a unified loss. |
| Outcome: | Experiments on the Spider and BIRD benchmarks show that JOLT-SQL achieves state-of-the-art execution accuracy among comparable-size open-source models. |
Understanding New-Knowledge-Induced Factual Hallucinations in LLMs: Analysis and Interpretation (2026.findings-acl)
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| Challenge: | Prior studies have shown that fine-tuning on new knowledge can induce factual hallucinations in large language models (LLMs), leading to incorrect outputs when evaluated on previously known information. |
| Approach: | They propose to conduct a fine-grained analysis of large language models using a dataset Biography-Reasoning and QA and knowledge reasoning tasks to understand their findings. |
| Outcome: | The proposed model is able to perform a range of downstream tasks without requiring a large amount of knowledge and is compared with a control dataset. |
Revisiting Demonstration Selection Strategies in In-Context Learning (2024.acl-long)
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| Challenge: | Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL). |
| Approach: | They propose a data- and model-dependent method to select models using in-context learning, TopK + ConE, and propose unified explanations for the effectiveness of previous methods. |
| Outcome: | The proposed method improves language understanding and generation tasks with different model scales. |
Task-aware Contrastive Mixture of Experts for Quadruple Extraction in Conversations with Code-like Replies and Non-opinion Detection (2025.emnlp-main)
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| Challenge: | Applying Large Language Models (LLMs) for this specific task presents two primary challenges: the accurate extraction of multiple elements and the understanding of complex dialogue reply structure. |
| Approach: | They propose a novel LLM-based multi-task approach to extract sentiment quadruples from conversations by integrating expert-level contrastive loss within task-oriented mixture of experts layer. |
| Outcome: | The proposed method outperforms existing fine-tuning techniques in terms of accuracy and computational efficiency. |
HealMe: Harnessing Cognitive Reframing in Large Language Models for Psychotherapy (2024.acl-long)
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Mengxi Xiao, Qianqian Xie, Ziyan Kuang, Zhicheng Liu, Kailai Yang, Min Peng, Weiguang Han, Jimin Huang
| Challenge: | Large Language Models (LLMs) can be used in psychotherapy to overcome challenges such as shame, distrust, and resource scarcity. |
| Approach: | They propose a cognitive reframing therapy method that uses empathetic dialogue to address deep-rooted negative thoughts and fosters rational, balanced perspectives. |
| Outcome: | The proposed model outperforms other models in terms of empathy, guidance, and logical coherence, demonstrating its effectiveness and potential positive impact on psychotherapy. |
GenWebNovel: A Genre-oriented Corpus of Entities in Chinese Web Novels (2025.coling-main)
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| Challenge: | Existing literature on nested entity recognition is insufficient partly due to insufficient annotated data. |
| Approach: | They propose a method that utilizes a pre-trained language model as an In-context learning example retriever to boost the performance of large language models. |
| Outcome: | The proposed method significantly enhances entity recognition, matching state-of-the-art (SOTA) models without additional training data. |
RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering (2024.emnlp-main)
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Rujun Han, Yuhao Zhang, Peng Qi, Yumo Xu, Jenyuan Wang, Lan Liu, William Yang Wang, Bonan Min, Vittorio Castelli
| Challenge: | Existing datasets for question answering based on retrieval augmented generation (RAG-QA) are either constructed using a single source corpus or consist of short extractive answers, which fall short of evaluating large language model (LLM) based RAG-QA systems on cross-domain generalization. |
| Approach: | They propose a dataset that integrates short extractive answers from multiple documents into a single coherent narrative. |
| Outcome: | The proposed dataset integrates short extractive answers from multiple documents into a single coherent narrative, covering 26K queries and large corpora across seven different domains. |
A Challenge Dataset and Effective Models for Conversational Stance Detection (2024.lrec-main)
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| Challenge: | stance detection studies focus on evaluating stances within individual instances, hindering progress of conversational stance analysis. |
| Approach: | They propose a multi-turn conversation stance detection dataset that encompasses multiple targets for conversational stance detector. |
| Outcome: | The proposed dataset encompasses multiple targets for conversational stance detection. |
DialogueMMT: Dialogue Scenes Understanding Enhanced Multi-modal Multi-task Tuning for Emotion Recognition in Conversations (2025.coling-main)
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| Challenge: | Existing ERC methods fail to handle emotional cues from both visual sources and discourse structures due to the complexity of visual scenes and contextual dependencies in conversations. |
| Approach: | They propose a framework for Emotion Recognition in conversations that utilizes multi-task instruction tuning to enhance the model's understanding of multi-modal dialogue scenes. |
| Outcome: | The proposed framework outperforms existing state-of-the-art models on three benchmark ERC datasets and is based on a video-language connector and a chain-of thought strategy. |
Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion (2023.findings-acl)
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| Challenge: | Existing methods for temporal knowledge graph completion (TKGC) focus on extracting information from timestamps and insufficiently utilizing implied information in relations. |
| Approach: | They propose a temporal knowledge graph completion model with prompts that converts quadruples into pre-trained language inputs and prompts to make coherent sentences with implicit semantic information. |
| Outcome: | The proposed model can make coherent sentences with implicit semantic information. |
Neural Sparse Topical Coding (P18-1)
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| Challenge: | Topic models with sparsity enhancement are effective at learning discriminative and coherent latent topics of short texts. |
| Approach: | They propose a novel sparsity-enhanced topic model with back propagation that replaces the inference process with the back propagations, making it easy to explore extensions. |
| Outcome: | The proposed model outperforms existing methods on Web Snippet and 20Newsgroups datasets. |
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)
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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen
| Challenge: | a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented. |
| Approach: | They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs). |
| Outcome: | The proposed library is based on extensive experiments in a variety of evaluation settings. |
Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models (2024.emnlp-main)
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Zhengxuan Wu, Yuhao Zhang, Peng Qi, Yumo Xu, Rujun Han, Yian Zhang, Jifan Chen, Bonan Min, Zhiheng Huang
| Challenge: | Modern language models fail to follow human instructions while being faithful . a trade-off exists between instruction following and faithfulness when training LMs . |
| Approach: | They propose a method that relies on Reject-sampling by Self-instruct with Continued Fine-tuning to train LMs to follow human instructions while being faithful. |
| Outcome: | The proposed method outperforms vanilla MTL with high-quality data, but with significantly smaller data. |
CaDRL: Document-level Relation Extraction via Context-aware Differentiable Rule Learning (2025.coling-main)
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Kunli Zhang, Pengcheng Wu, Bohan Yu, Kejun Wu, Aoze Zheng, Xiyang Huang, Chenkang Zhu, Min Peng, Hongying Zan, Yu Song
| Challenge: | Existing methods for document-level relation extraction (DocRE) lack logic and transparency. |
| Approach: | They propose a Context-aware differentiable rule learning framework that learns the doc-specific logical rule to avoid suboptimal constraints. |
| Outcome: | The proposed framework outperforms existing rule-based frameworks on three DocRE datasets. |
Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion (2025.coling-main)
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| Challenge: | Empirical evidence suggests that LLMs perform worse than conventional KGC approaches. |
| Approach: | They propose a filter-then-generate paradigm and a multiple-choice question format to harness the capability of LLMs while mitigating the issue casused by hallucinations. |
| Outcome: | The proposed method achieves substantial performance gain compared to existing state-of-the-art methods. |
Towards Making the Most of ChatGPT for Machine Translation (2023.findings-emnlp)
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| Challenge: | Prior studies have shown that ChatGPT achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resourced and distant-language-pairs translation. |
| Approach: | They propose task-specific prompts and domain-specific prompts which are based on task information and domain information and a task-specific prompt. |
| Outcome: | The proposed prompts improve the performance of ChatGPT in complex tasks and generate hallucinations for non-English-centric tasks. |
CiteEval: Principle-Driven Citation Evaluation for Source Attribution (2025.acl-long)
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Yumo Xu, Peng Qi, Jifan Chen, Kunlun Liu, Rujun Han, Lan Liu, Bonan Min, Vittorio Castelli, Arshit Gupta, Zhiguo Wang
| Challenge: | Current evaluation frameworks rely on NLI to assess binary or ternary support from cited sources, which is suboptimal for citation evaluation. |
| Approach: | They propose a citation evaluation framework based on fine-grained citation ratings within a broad context and construct a multi-domain benchmark with high-quality human annotations. |
| Outcome: | The proposed framework provides a high-quality human annotation benchmark and a suite of model-based metrics that exhibit strong correlation with human judgments. |
BiasFilter: An Inference-Time Debiasing Framework for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods for debiasing large language models incur high human and computational costs and are limited in their effectiveness. |
| Approach: | They propose a model-agnostic, inference-time debiasing framework that enforces fairness by filtering generation outputs in real time. |
| Outcome: | The proposed framework mitigates social bias across a range of LLMs while preserving overall generation quality. |
Semi-supervised Domain Adaptation for Dependency Parsing (P19-1)
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| Challenge: | Currently, most studies on cross-domain parsing focus on unsupervised domain adaptation . however, unsupervised approaches make limited progress due to the intrinsic difficulty of both domain adaptation and parse. |
| Approach: | They propose a semi-supervised domain adaptation problem for Chinese dependency parsing by using newly-annotated large-scale domain-aware datasets. |
| Outcome: | The proposed method is more effective than direct corpus concatenation and multi-task learning. |