13 papers
Aligning LLMs with Sparse Retrieval via Adaptive Sparse Supervision
Tongna Chen, Minghan Li, Guodong Zhou
While large language models (LLMs) offer strong semantic representations, their application to first-stage retrieval incurs substantial latency and storage costs. Neural sparse ret…
XViewRank: Overlap-Conditioned Score Fusion for LLM-Augmented Sparse Retrieval
Xinxuan Lv, Minghan Li, Guodong Zhou
Large language models can reduce vocabulary mismatch in sparse retrieval by generating alternative views of a query. Once these views are searched independently, however, their ran…
Does the Linguistic Register of Thinking Matter? Wenyan as a Compact Reasoning Register for Chinese LLMs
Minghan Li, Guodong Zhou
Visible reasoning traces of Chinese reasoning-oriented LLMs are typically written in modern vernacular Chinese, which may be verbose under limited decoding budgets. We ask whether…
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…
GenState-AI: State-Aware Dataset for Text-to-Video Retrieval on AI-Generated Videos
Minghan Li, Tongna Chen, Tianrui Lv +3
Existing text-to-video retrieval benchmarks often permit matching from static appearance, making it difficult to determine whether models capture state changes and final outcomes.…
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…
RFKG-CoT: Relation-Driven Adaptive Hop-count Selection and Few-Shot Path Guidance for Knowledge-Aware QA
Chao Zhang, Minghan Li, Tianrui Lv +1
Large language models (LLMs) often generate hallucinations in knowledge-intensive QA due to parametric knowledge limitations. While existing methods like KG-CoT improve reliability…
A Survey of Long-Document Retrieval in the PLM and LLM Era
Minghan Li, Miyang Luo, Tianrui Lv +4
The proliferation of long-form documents presents a fundamental challenge to information retrieval (IR), as their length, dispersed evidence, and complex structures demand speciali…
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…
Efficient Long-Document Reranking via Block-Level Embeddings and Top-k Interaction Refinement
Minghan Li, Eric Gaussier, Guodong Zhou
Dense encoders and LLM-based rerankers struggle with long documents: single-vector representations dilute fine-grained relevance, while cross-encoders are often too expensive for p…
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 an attention analysis indicati…