3 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.IR2025★ 1 cited
How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective
Qi Liu, Jiaxin Mao, Ji-Rong Wen
Recent studies have shown that large language models (LLMs) can assess relevance and support information retrieval (IR) tasks such as document ranking and relevance judgment genera…
cs.IR2025★ 1 cited
LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking
Qi Liu, Haozhe Duan, Yiqun Chen +3
Utilizing large language models (LLMs) for document reranking has been a popular and promising research direction in recent years, many studies are dedicated to improving the perfo…
cs.AI2024★ 3 cited
Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval
Shengjie Ma, Qi Chu, Jiaxin Mao +3
Determining which legal cases are relevant to a given query involves navigating lengthy texts and applying nuanced legal reasoning. Traditionally, this task has demanded significan…