activity
20232026
most citedLarge Language Models for Information Retrieval: A Survey

98 citations · 100 across the 7 of their papers we have counts for

collaborators

8 papers

cs.IR2026

Training Documents Reranker with Search Rubrics for Deep Research Agent

Wenhan Liu, Yu Lu, Qiaolin Xia +8

Retrieval systems help deep research agents generate high-quality answers by providing relevant documents. However, existing retrievers typically select documents through relevance…

cs.IR2026

SumRank: Aligning Summarization Models for Long-Document Listwise Reranking

Jincheng Feng, Wenhan Liu, Zhicheng Dou

Large Language Models (LLMs) have demonstrated superior performance in listwise passage reranking task. However, directly applying them to rank long-form documents introduces both…

cs.IR2026

Agentic-R: Learning to Retrieve for Agentic Search

Wenhan Liu, Xinyu Ma, Yutao Zhu +4

Agentic search has recently emerged as a powerful paradigm, where an agent interleaves multi-step reasoning with on-demand retrieval to solve complex questions. Despite its success…

cs.IR2025

ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability

Wenhan Liu, Xinyu Ma, Weiwei Sun +4

Large Language Model (LLM) based listwise ranking has shown superior performance in many passage ranking tasks. With the development of Large Reasoning Models (LRMs), many studies…

cs.CL2025

CoRanking: Collaborative Ranking with Small and Large Ranking Agents

Wenhan Liu, Xinyu Ma, Yutao Zhu +4

Large Language Models (LLMs) have demonstrated superior listwise ranking performance. However, their superior performance often relies on large-scale parameters (\eg, GPT-4) and a…

cs.IR2024★ 1 cited

Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models

Wenhan Liu, Xinyu Ma, Yutao Zhu +4

Large Language Models (LLMs) have shown exciting performance in listwise passage ranking. Due to the limited input length, existing methods often adopt the sliding window strategy.…

cs.IR2024★ 1 cited

DemoRank: Selecting Effective Demonstrations for Large Language Models in Ranking Task

Wenhan Liu, Yutao Zhu, Zhicheng Dou

Recently, there has been increasing interest in applying large language models (LLMs) as zero-shot passage rankers. However, few studies have explored how to select appropriate in-…

cs.CL2023★ 98 cited

Large Language Models for Information Retrieval: A Survey

Yutao Zhu, Huaying Yuan, Shuting Wang +7

As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve…