98 citations · 100 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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.…
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-…