Papers by Mingchen Li
Harmful Factuality: LLMs Correcting What They Shouldn’t (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) are trained for factual accuracy, but can conflict with the critical demand for source fidelity. |
| Approach: | They propose a reproducible framework to elicit and measure HFH using controlled entity-level perturbations and strategic entity selection. |
| Outcome: | The proposed framework reduces HFH rates by 50% across summarization, rephrasing, and QA tasks. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
Semantic Structure Based Query Graph Prediction for Question Answering over Knowledge Graph (2022.coling-1)
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| Challenge: | Existing approaches for query graph generation ignore the semantic structure of a question . Existing methods ignore the structure of the question, resulting in noisy query graph candidates. |
| Approach: | They propose to build query graphs from natural language questions to predict semantic structure of a question. |
| Outcome: | The proposed method can predict the semantic structure of a question using six semantic structures from common questions in KGQA. |
A Hierarchical N-Gram Framework for Zero-Shot Link Prediction (2022.findings-emnlp)
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| Challenge: | Existing approaches to zero-shot link prediction use textual features of relations as auxiliary information to improve the encoded representation. |
| Approach: | They propose a Hierarchical N-gram framework for Zero-Shot Link Prediction that leverages character n-gram information for ZSLP. |
| Outcome: | The proposed method achieves state-of-the-art on two standard ZSLP datasets. |
Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction (2026.findings-acl)
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| Challenge: | Existing approaches to ground large language models in external knowledge are limited by hallucinations and a lack of granular medical context. |
| Approach: | They propose a framework that replaces external retrieval with internal, key-based knowledge access by encoding clinical information directly into the model’s parameter space. |
| Outcome: | The proposed framework achieves state-of-the-art performance across four benchmark healthcare outcome prediction datasets. |
RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs in Medicine (2026.findings-acl)
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Jiatan Huang, Mingchen Li, Zonghai Yao, Dawei Li, Yuxin Zhang, Zhichao Yang, Yongkang Xiao, Feiyun Ouyang, Xiaohan Li, Shuo Han, Hong yu
| Challenge: | Existing methods for retrieving medical textual knowledge Graphs struggle to perform well, a study finds . existing methods struggle to provide accurate answers to complex questions, he says . |
| Approach: | They synthesize user queries integrating diverse topological structures, relational information, and complex textual descriptions. |
| Outcome: | a new dataset for medical textual knowledge graphs shows that existing methods struggle to perform well . main bottlenecks lie in the scarcity of existing medical TKGs and the limited expressiveness of their topological structures . |
DP-GTR: Differentially Private Prompt Protection via Group Text Rewriting (2025.findings-emnlp)
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| Challenge: | Existing methods for prompt privacy focus on document-level rewriting, neglecting rich, multi-granular representations of text. |
| Approach: | a framework that leverages local differential privacy and composition theorem via group text rewriting is proposed . the framework is compatible with existing rewrite techniques and is publicly available at anonymous.4open.science for reproducibility. |
| Outcome: | DP-GTR is the first framework to integrate document-level and word-level information while exploiting in-context learning to improve privacy and utility. |
Knowledge Graph Pooling and Unpooling for Concept Abstraction (2025.coling-main)
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| Challenge: | Knowledge graph embedding (KGE) aims to embed entities and relations as vectors in a continuous space. |
| Approach: | They propose a framework with KG Pooling and unpooling and Contrastive Learning to abstract and encode latent concepts for better KG prediction. |
| Outcome: | The proposed framework outperforms baselines on link prediction task. |
VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning (2025.acl-demo)
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Yang Tan, Chen Liu, Jingyuan Gao, Wu Banghao, Mingchen Li, Ruilin Wang, Lingrong Zhang, Huiqun Yu, Guisheng Fan, Liang Hong, Bingxin Zhou
| Challenge: | Pre-trained protein language models have been used in protein engineering, but their adoption is limited due to data collection, task benchmarking, and application challenges. |
| Approach: | They propose a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs. |
| Outcome: | The proposed engine integrates biological data retrieval, task benchmarking, and modular fine-tuning of PLMs. |