Papers by Chenhao Ma
Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models (2026.findings-acl)
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Chenhao Li, Dandan Song, Changzhi Zhou, Jun Yang, Yuhang Tian, Huipeng Ma, Guangyuan Feng, Luan Zhang, Xudong Li, Ke Duan
| Challenge: | Large language models are trained on static corpora but deployed in a dynamic world . a foundational tension remains between time and the ability to understand it . |
| Approach: | They formalize temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers. |
| Outcome: | The proposed framework formalizes temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers . the framework induces four temporal information regimes corresponding to internal reasoning, answer recency, premise anchoring, and genuine world indeterminacy . |
Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) driven by In-Context Learning (ICL) have improved performance of text-to-SQL. |
| Approach: | They propose a strategy to mitigate hallucinations in large language models driven by In-Context Learning (ICL) they propose TA-SQL, a text-to-Sql framework that encourages LLMs to take advantage of similar tasks rather than starting from scratch. |
| Outcome: | The proposed framework improves the performance of the GPT-4 model by 21.23% on BIRD dev. |
Multi-Hop Knowledge Editing via Critic-Guided Multi-Agent Reasoning (2026.findings-acl)
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Xudong Li, Yuhang Tian, Dandan Song, Zhijing Wu, Shuhao Zhang, Jun Yang, Yongyu Huo, Changzhi Zhou, Xinyu Zhang, Chenhao Li, Huipeng Ma, Luan Zhang, Yan Xu, Qian Liu
| Challenge: | Existing knowledge editing methods rely on unidirectional, feed-forward pipelines . a minor retrieval error or logical mismatch at an early hop can become a silent failure . |
| Approach: | They propose a framework for closed-loop post-edit reasoning that uses a Critic agent to verify coherence and step-wise correctness. |
| Outcome: | Experiments on MQuAKE-2002 and MQuADE-hard show that CARE effectively mitigates error propagation . a minor retrieval error or logical mismatch at an early hop can become a silent failure . |
SHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQL (2025.acl-long)
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| Challenge: | Existing approaches to self-correct text-to-SQL fail to demonstrate underlying reasoning path . authors propose **SHARE**, a self-revolution assistant for text-based error correction . |
| Approach: | They propose a "SHARE" assistant that enables LLMs to perform more precise error localization and efficient correction. |
| Outcome: | The proposed assistant performs more precise error localization and efficient correction for monolithic SQL queries. |
Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering (2026.findings-acl)
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Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, Jing Jiang
| Challenge: | Existing retrieval-augmented approaches focus on ignoring the structural information of the Knowledge Base (KB) and the question. |
| Approach: | They propose a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question’s structure, along with semantic relevance. |
| Outcome: | Experiments on GrailQA, WebQSP, and GraphQuestions show that the proposed framework achieves state-of-the-art performance. |
Micro-Act: Mitigate Knowledge Conflict in Question Answering via Actionable Self-Reasoning (2025.acl-long)
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| Challenge: | Existing approaches to mitigate knowledge conflict by comparing two knowledge sources can overwhelm LLMs with extraneous or lengthy contexts. |
| Approach: | They propose a framework that decomposes knowledge into fine-grained comparisons . they propose 'Micro-Act' framework that allows for reasoning beyond the superficial context . |
| Outcome: | The proposed framework achieves significant increase in QA accuracy over state-of-the-art baselines on five benchmark datasets. |
CompKBQA: Component-wise Task Decomposition for Knowledge Base Question Answering (2025.emnlp-main)
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Yuhang Tian, Dandan Song, Zhijing Wu, Pan Yang, Changzhi Zhou, Jun Yang, Hao Wang, Huipeng Ma, Chenhao Li, Luan Zhang
| Challenge: | Existing knowledge base question answering methods struggle with complex queries. |
| Approach: | They propose a framework that optimizes the process of fine-tuning a LLM for generating logical forms by enabling it to learn relevant sub-tasks like skeleton generation, topic entity generation, and relevant relations generation. |
| Outcome: | The proposed framework achieves state-of-the-art on two benchmark KBQA datasets, WebQSP and CWQ. |