Papers by Zhumin Chen
Reinforced Efficient Reasoning via Semantically Diverse Exploration (2026.acl-long)
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Ziqi Zhao, Zhaochun Ren, Jiahong Zou, Liu Yang, Zhiwei Xu, Xuri Ge, Zhumin Chen, Xinyu Ma, Daiting Shi, Shuaiqiang Wang, Dawei Yin, Xin Xin
| Challenge: | Existing methods for reinforcement learning with verifiable rewards suffer from limited exploration diversity and inefficient reasoning. |
| Approach: | They propose a method that rewards concise and correct reasoning while penalizing unnecessarily long reasoning chains. |
| Outcome: | Extensive experiments on Qwen and Llama models validate the effectiveness and efficiency of ROSE. |
Self-Supervised Position Debiasing for Large Language Models (2024.findings-acl)
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| Challenge: | Existing methods for debiasing large language models require external bias knowledge or annotated non-biased samples, which is lacking for position debiases. |
| Approach: | They propose a self-supervised position debiasing framework that leverages unsupervised responses from pre-trained LLMs for debiazing without external bias knowledge. |
| Outcome: | The proposed framework outperforms existing methods in mitigating three types of position biases on eight datasets and five tasks. |
Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems (2025.findings-emnlp)
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Minghang Zhu, Zhengliang Shi, Zhiwei Xu, Shiguang Wu, Lingjie Wang, Pengjie Ren, Zhaochun Ren, Zhumin Chen
| Challenge: | Existing methods for fine-tuning agents are often inadequate . a multi-agent system can solve complex tasks by dividing responsibilities among specialized agents . |
| Approach: | a new framework is proposed to improve agents collaboration through iterative alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on held-in and held-out tasks. |
Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering (2024.acl-long)
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| Challenge: | Existing approaches to solve multi-hop question are constrained by the retriever and the noise in the retrieved documents. |
| Approach: | They propose a framework that integrates parametric knowledge of large language models with external documents to solve a multi-hop question. |
| Outcome: | The proposed framework is based on the parametric knowledge of LLMs and external documents to solve a multi-hop question. |
Syllogistic Reasoning for Legal Judgment Analysis (2023.emnlp-main)
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Wentao Deng, Jiahuan Pei, Keyi Kong, Zhe Chen, Furu Wei, Yujun Li, Zhaochun Ren, Zhumin Chen, Pengjie Ren
| Challenge: | Legal judgment assistants are developing fast due to impressive progress of large language models. |
| Approach: | They construct and manually correct a syllogistic reasoning dataset for legal judgment analysis using large language models as benchmarks. |
| Outcome: | The proposed dataset contains 11,239 criminal cases covering 4 criminal elements, 80 charges and 124 articles. |
Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues (2023.tacl-1)
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| Challenge: | Existing answer selection models require large amounts of labeled data to produce accurate answers. |
| Approach: | They propose intent-calibrated self-training to calibrate answer labels using labeled data . they propose intentcalibration to improve quality of pseudo answer labels . |
| Outcome: | The proposed intent-calibrated answer selection paradigm outperforms baselines with 1%, 5%, and 10% labeled data on two benchmark datasets. |
Learning to Ask Conversational Questions by Optimizing Levenshtein Distance (2021.acl-long)
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| Challenge: | Existing methods for estimating maximum likelihood are limited by easily learned tokens . Existing systems that generate questions based on dialogue context are limited in their ability to learn tokens. |
| Approach: | They propose a framework that optimizes the minimum Levenshtein distance through explicit editing actions. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two benchmark datasets and generalizes well on unseen data. |
KELE: Residual Knowledge Erasure for Enhanced Multi-hop Reasoning in Knowledge Editing (2025.findings-emnlp)
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| Challenge: | Existing knowledge editing techniques show limitations when applied to multi-hop reasoning . residual single-hop knowledge causes edited models to revert to original answers . |
| Approach: | They propose a knowledge editing method that incorporates a Knowledge Erasure mechanism for Large language model Editing (KELE) they propose an erasure function for residual knowledge and an injection function for new knowledge . |
| Outcome: | The proposed method significantly improves multi-hop reasoning capability of edited models. |
UMSE: Unified Multi-scenario Summarization Evaluation (2023.findings-acl)
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| Challenge: | Summarization quality evaluation is a non-trivial task in text summarization. |
| Approach: | They propose a unified multi-scenario summarization evaluation model that shares cross-sceenario knowledge and uses a self-supervised training paradigm to optimize the model without extra human labeling. |
| Outcome: | The proposed model can achieve comparable performance with existing methods for three evaluation scenarios. |
Improving Multi-label Malevolence Detection in Dialogues through Multi-faceted Label Correlation Enhancement (2022.acl-long)
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| Challenge: | Current methods for detecting dialogue malevolence neglect label correlation. |
| Approach: | They propose to crowdsource a multi-label dataset for detecting malevolent dialogue responses and a model with label correlation enhanced CRF to measure the correlation between malevolence and negative emotions. |
| Outcome: | The proposed model outperforms the best performing baseline method on precision, recall, F1, and Jaccard score by 16.1%, 11.9%, 12.0%, and 6.1% on malevolence. |
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents (2023.emnlp-main)
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Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, Zhaochun Ren
| Challenge: | Existing work utilizes generative LLMs for Information Retrieval (IR) rather than direct passage ranking. |
| Approach: | They investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR and use a test set to verify the model’s ability to rank unknown knowledge. |
| Outcome: | The proposed model outperforms a 3B supervised model on the BEIR benchmark. |
Task Knowledge Injection via Interpolations and Reinstatement for Large Language Model Generalization (2025.findings-acl)
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| Challenge: | Pre-trained large language models have been widely adopted to elicit their superior performance on downstream tasks, but instruction tuning may overfit them to specific task formats, compromising their generalization on unseen tasks. |
| Approach: | They propose to inject latent task adaptation and knowledge reinstatement into large language models to mitigate spurious correlations between inputs and targets. |
| Outcome: | The proposed method improves generalization on in-domain and out-of-domain unseen tasks. |
EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation (2020.coling-main)
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| Challenge: | Existing work on empathetic dialogue generation fails to capture the nuances of human emotion and consider the potential of user feedback. |
| Approach: | They propose a multi-resolution adversarial model - EmpDG - to generate more empathetic responses by exploiting both coarse-grained dialogue-level and fine-grounded token-level emotions. |
| Outcome: | The proposed model outperforms the state-of-the-art models in both content quality and emotion perceptivity. |
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning (2024.acl-long)
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Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten Rijke, Zhumin Chen, Jiahuan Pei
| Challenge: | Large language models (LLMs) are the default paradigm for natural language processing (NLP) as the models’ scale and the diversity of tasks increase, fine-tuning becomes infeasible. |
| Approach: | They propose to freeze original pretrained weights and train a group of mini LoRAs with only a small number of parameters and reduce their rank by 8 times . |
| Outcome: | The proposed model uses fewer trainable parameters while maintaining a higher rank, thereby offering improved performance potential. |
Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse (2026.acl-long)
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Chi Zhang, Mengqi Zhang, Xiaotian Ye, Runxi Cheng, Zisheng Zhou, Ying Zhou, Pengjie Ren, Zhumin Chen
| Challenge: | Existing approaches to reducing the effects of knowledge editing are insufficiently understood. |
| Approach: | They propose a plug-and-play framework that preserves the dominant subspace of the original weights and analyzes parameter updates in the spectral basis of the weights. |
| Outcome: | The proposed framework improves editing efficacy while preserving general abilities under long-horizon sequential editing, including extreme settings with up to 20,000 edits. |
CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges (2025.acl-long)
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| Challenge: | Empirical evaluations of large language models demonstrate that they improve performance in a wide range of tasks. |
| Approach: | They propose a label-free method for mitigating selection bias during inference by reformulating debiasing as an optimization task. |
| Outcome: | The proposed method mitigates selection bias and improves performance compared to existing methods. |
Knowledge Graph Enhanced Large Language Model Editing (2024.emnlp-main)
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| Challenge: | Existing methods for editing large language models struggle to track and incorporate changes in knowledge associated with edits, which limits the generalization ability of post-edit LLMs in processing edited knowledge. |
| Approach: | They propose a model editing method that leverages knowledge graphs to enhance LLM editing by capturing changes in associated knowledge by constructing an external graph. |
| Outcome: | The proposed method improves the generalization ability of LLMs in processing edited knowledge. |
UIPE: Enhancing LLM Unlearning by Removing Knowledge Related to Forgetting Targets (2025.findings-emnlp)
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| Challenge: | Existing methods to unlearning large language models focus on forgetting target data while overlooking the impact of logically related knowledge on the effectiveness of unlearning. |
| Approach: | They propose a method that removes knowledge highly correlated with the forgetting targets and a technique that remove logically related knowledge from the model. |
| Outcome: | The proposed method significantly improves the performance of the proposed method on the TOFU and WMDP benchmarks. |
Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning (2023.findings-emnlp)
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Yougang Lyu, Jitai Hao, Zihan Wang, Kai Zhao, Shen Gao, Pengjie Ren, Zhumin Chen, Fang Wang, Zhaochun Ren
| Challenge: | Existing methods for predicting judgment results for multiple defendants are ineffective. |
| Approach: | They propose a method to predict the judgment results for each defendant in multi-defendant cases . they formalize the multi-diffendant judgment process as hierarchical reasoning chains . |
| Outcome: | The proposed method can predict the judgment results for multiple defendants in multi-defendant cases. |
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features (2023.findings-emnlp)
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| Challenge: | Few-shot named entity recognition methods struggle with out-of-domain (OOD) examples due to their reliance on manual labeling for the target domain. |
| Approach: | They propose a framework to enable generalization to an unseen target domain with only a few labeled examples. |
| Outcome: | The proposed framework achieves significant performance improvements on in-domain and cross-domain datasets. |
MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter (2024.acl-long)
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| Challenge: | Parameter-Efficient Fine-tuning (PEFT) methods are limited on knowledge-intensive tasks due to the limited number of trainable parameters. |
| Approach: | They propose a mechanism that fine-tunes Large Language Models with larger adapters . they store and update the parameters of larger adapter adapters on the CPU . |
| Outcome: | The proposed method achieves comparable results to those obtained with larger memory capacities over the limited bandwidth of PCI Express (PCIe). |
Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Reality (2024.findings-acl)
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Jiahuan Pei, Irene Viola, Haochen Huang, Junxiao Wang, Moonisa Ahsan, Fanghua Ye, Jiang Yiming, Yao Sai, Di Wang, Zhumin Chen, Pengjie Ren, Pablo Cesar
| Challenge: | a fine-grained, comprehensive understanding of multimodal environments remains under-explored. |
| Approach: | They propose an automated workflow for integrating AI agents into extended reality (XR) they propose a cerebral language agent that integrates LLM with memory, planning, and interaction with XR tools and a vision-language agent . |
| Outcome: | The proposed workflow integrates AI agents seamlessly into extended reality (XR) applications for fine-grained training. |
Towards a Unified Framework for Reference Retrieval and Related Work Generation (2023.findings-emnlp)
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| Challenge: | Existing methods for related work generation use human-annotated references as information sources. |
| Approach: | They propose a model which combines reference retrieval and related work generation processes in a unified framework based on the large language model. |
| Outcome: | The proposed model outperforms the state-of-the-art models on two wide-applied datasets. |
Answering Ambiguous Questions via Iterative Prompting (2023.acl-long)
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| Challenge: | Empirical studies show that AmbigPrompt achieves state-of-the-art or competitive results while using less memory and having a lower inference latency than competing approaches. |
| Approach: | They propose an answering model with a prompting model to address imperfections in open-domain question answering . Empirical studies show AmbigPrompt achieves state-of-the-art or competitive results . |
| Outcome: | The proposed framework improves on two commonly-used open benchmarks and achieves state-of-the-art or competitive results while using less memory and having a lower inference latency. |
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models (2024.emnlp-main)
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Yougang Lyu, Lingyong Yan, Shuaiqiang Wang, Haibo Shi, Dawei Yin, Pengjie Ren, Zhumin Chen, Maarten Rijke, Zhaochun Ren
| Challenge: | Large language models (LLMs) are a default solution for many natural language processing tasks. |
| Approach: | They propose a knowledge-aware fine-tuning method to improve LLMs' knowledge awareness . they propose augmentation and comparison stages to improve accuracy and reliability . |
| Outcome: | The proposed method generates more facts with less factual error rate under fine-grained facts evaluation. |
Trustworthy Medical Question Answering: An Evaluation-Centric Survey (2025.emnlp-main)
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Yinuo Wang, Baiyang Wang, Robert Mercer, Frank Rudzicz, Sudipta Singha Roy, Pengjie Ren, Zhumin Chen, Xindi Wang
| Challenge: | achieving comprehensive trustworthiness in medical QA poses significant challenges due to complexity of healthcare data, critical nature of clinical scenarios, and multifaceted dimensions of trustworthy AI. |
| Approach: | They examine six key dimensions of trustworthiness in medical QA . they compare how each dimension is evaluated in existing LLM-based systems . |
| Outcome: | The findings show that large language models have improved patient safety and effectiveness . the models exhibit critical trust failures when deployed in clinical settings . |