Papers by Mingjie Zhong
Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning. |
| Approach: | They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning. |
| Outcome: | Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines. |
Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting (2025.coling-industry)
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| Challenge: | Existing approaches to relevance modeling have lacked generalization and accuracy . recent studies have focused on capturing the semantic relationships between queries and items . |
| Approach: | They propose a framework that integrates world knowledge stored in LLMs with specialized domain knowledge represented by user behavior data for promising performance. |
| Outcome: | The proposed framework can handle full-scale search traffics of Alipay with acceptable cost and latency. |
GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection (2026.eacl-long)
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| Challenge: | Existing methods focus on layer-selective and data-selectory fine-tuning, but ignore the fact that different data points contribute varying degrees to distinct model layers. |
| Approach: | They propose a method that performs selective fine-tuning at both data and layer dimensions as integral components of a unified optimization strategy. |
| Outcome: | Experiments show that the proposed method outperforms baseline methods in terms of performance and performance. |
Why Are We Moral? An LLM-based Agent Simulation Approach to the Study of Moral Evolution (2026.acl-long)
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Zhou Ziheng, Huacong Tang, Mingjie Bi, Wanying He, Fang Sun, Yizhou Sun, Ying Nian Wu, Demetri Terzopoulos, Yipeng Kang, Fangwei Zhong
| Challenge: | Existing models of moral evolution must abstract away cognitive processes . et al. (2017): evolution of morality presents a puzzle: natural selection favors selfish . |
| Approach: | They propose an LLM-based agent simulation framework that manipulates cognitive factors to understand moral evolution. |
| Outcome: | The proposed model exploits cognitive realism to explore moral evolution in a hunter-gatherer society. |