4 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…
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.…
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…
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…