I am currently a faculty member at the School of Computer Science and Technology, Soochow University. My research lies at the intersection of information retrieval, natural language processing, and large language models. I am particularly interested in conversational search, retrieval-augmented generation, long-document retrieval and reranking, legal information retrieval, and efficient and trustworthy LLM-based systems.

I received my B.S. degree from Xi’an Jiaotong University, my M.S. degree from Xidian University, and my Ph.D. degree from Université Grenoble Alpes, where I was advised by Prof. Eric Gaussier. If you are interested in academic collaboration, student supervision, or research discussions, please feel free to contact me at [email].

🔥 News

  • 2022.02:  🎉🎉 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vivamus ornare aliquet ipsum, ac tempus justo dapibus sit amet.
  • 2022.02:  🎉🎉 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vivamus ornare aliquet ipsum, ac tempus justo dapibus sit amet.

📝 Publications

Information Fusion 2026
Dual-Layer Prompt Ensembles

Dual-Layer Prompt Ensembles: Leveraging System-and User-Level Instructions for Robust LLM-Based Query Expansion and Rank Fusion

Minghan Li, Ercong Nie, Huiping Huang, Xinxuan Lv, Guodong Zhou

Information Fusion, 2026

  • Robust LLM-based query expansion and rank fusion with dual-layer prompt ensembles.
  • Corresponding author and first author.
ACL 2026
GLIER

GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval

Minghan Li, Tianrui Lv, Chao Zhang, Guodong Zhou

ACL 2026 Main Conference

  • A generative legal inference framework for legal case retrieval.
  • Combines latent legal variable inference with multi-view evidence ranking.
  • Corresponding author and co-first author.
ACL 2026
S2G-RAG

S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA

Minghan Li, Junjie Zou, Xinxuan Lv, Chao Zhang, Guodong Zhou

ACL 2026 Main Conference

  • An iterative RAG framework with structured sufficiency judgment and gap-guided retrieval.
  • Designed for multi-hop QA and evidence-aware iterative retrieval.
  • Corresponding author and co-first author.
TOIS 2023
TOIS 2023

The Power of Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval

Minghan Li, Diana Nicoleta Popa, Johan Chagnon, Yagmur Gizem Cinar, Eric Gaussier

ACM Transactions on Information Systems, 2023

  • Studies key-block selection for long document retrieval.
  • A representative journal paper on long-document IR.

📚 Conference and Journal Papers

  • AAAI 2026 RFKG-CoT: Relation-Driven Adaptive Hop-count Selection and Few-Shot Path Guidance for Knowledge-Aware QA, Chao Zhang, Minghan Li, Tianrui Lv, Guodong Zhou
  • COLING 2024 Domain Adaptation for Dense Retrieval and Conversational Dense Retrieval through Self-Supervision by Meticulous Pseudo-Relevance Labeling, Minghan Li, Eric Gaussier
  • SIGIR 2022 BERT-based Dense Intra-ranking and Contextualized Late Interaction via Multi-task Learning for Long Document Retrieval, Minghan Li, Eric Gaussier
  • AAAI 2022 Listwise Learning to Rank Based on Approximate Rank Indicators, Thibaut Thonet, Yagmur Gizem Cinar, Éric Gaussier, Minghan Li, Jean-Michel Renders
  • SIGIR 2021 KeyBLD: Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval, Minghan Li, Eric Gaussier
  • ICPR 2020 Learning to Rank for Active Learning: A Listwise Approach, Minghan Li, Xialei Liu, Joost van de Weijer, Bogdan Raducanu

🗂 Preprints and Surveys

  • arXiv 2026 Automatic In-Domain Exemplar Construction and LLM-Based Refinement of Multi-LLM Expansions for Query Expansion, Minghan Li, Ercong Nie, Siqi Zhao, Tongna Chen, Huiping Huang, Guodong Zhou
  • arXiv 2026 GenState-AI: State-Aware Dataset for Text-to-Video Retrieval on AI-Generated Videos, Minghan Li, Tongna Chen, Tianrui Lv, Yishuai Zhang, Suchao An, Guodong Zhou
  • arXiv 2026 Retrieval-Feedback-Driven Distillation and Preference Alignment for Efficient LLM-based Query Expansion, Minghan Li, Guodong Zhou
  • arXiv 2025 Enhanced Retrieval of Long Documents: Leveraging Fine-Grained Block Representations with Large Language Models, Minghan Li, Eric Gaussier, Guodong Zhou
  • arXiv 2025 A Survey of Long-Document Retrieval in the PLM and LLM Era, Minghan Li, Miyang Luo, Tianrui Lv, Yishuai Zhang, Siqi Zhao, Ercong Nie, Guodong Zhou
  • arXiv 2025 Query Expansion in the Age of Pre-trained and Large Language Models: A Comprehensive Survey, Minghan Li, Xinxuan Lv, Junjie Zou, Tongna Chen, Chao Zhang, Suchao An, Ercong Nie, Guodong Zhou
  • arXiv 2024 EviRerank: Adaptive Evidence Construction for Long-Document LLM Reranking, Minghan Li, Eric Gaussier, Juntao Li, Guodong Zhou

📖 Educations

  • 2020.10 - 2023.12, Ph.D. in Computer Science, Université Grenoble Alpes, France.
  • M.S. in Computer Technology, Xidian University, China.
  • B.E. in Information Engineering, Xi’an Jiaotong University, China.