8 papers · 1 filter
EviRerank: Adaptive Evidence Construction for Long-Document LLM Reranking
Minghan Li, Eric Gaussier, Juntao Li +1
Decoder-only LLM rerankers struggle with long documents: inference is costly and relevance signals can be diluted by irrelevant context. Motivated by a diagnostic attention analysi…
Query Expansion in the Age of Pre-trained and Large Language Models: A Comprehensive Survey
Minghan Li, Xinxuan Lv, Junjie Zou +5
Modern information retrieval must reconcile short, ambiguous queries with increasingly diverse and dynamic corpora. Query expansion (QE) remains a core technique for mitigating voc…
S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA
Minghan Li, Junjie Zou, Xinxuan Lv +2
Retrieval-Augmented Generation (RAG) grounds language models in external evidence, but multi-hop question answering remains difficult because iterative pipelines must control what…
GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval
Minghan Li, Tianrui Lv, Chao Zhang +1
The semantic gap between colloquial user queries and professional legal documents presents a fundamental challenge in Legal Case Retrieval (LCR). Existing dense retrieval methods t…
Retrieval-Feedback-Driven Distillation and Preference Alignment for Efficient LLM-based Query Expansion
Minghan Li, Guodong Zhou
Large language models have recently enabled a generative paradigm for query expansion, but their high inference cost makes direct deployment difficult in practical retrieval system…
Automatic In-Domain Exemplar Construction and LLM-Based Refinement of Multi-LLM Expansions for Query Expansion
Minghan Li, Ercong Nie, Siqi Zhao +3
Query expansion with large language models is promising but often relies on hand-crafted prompts, manually chosen exemplars, or a single LLM, making it non-scalable and sensitive t…