1 citations · 2 across the 7 of their papers we have counts for
8 papers
LLM-based Listwise Reranking under the Effect of Positional Bias
Jingfen Qiao, Jin Huang, Xinyu Ma +4
LLM-based listwise passage reranking has attracted attention for its effectiveness in ranking candidate passages. However, these models suffer from positional bias, where passages…
Model Editing for New Document Integration in Generative Information Retrieval
Zhen Zhang, Zihan Wang, Xinyu Ma +6
Generative retrieval (GR) reformulates the Information Retrieval (IR) task as the generation of document identifiers (docIDs). Despite its promise, existing GR models exhibit poor…
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…
The Mirage of Model Editing: Revisiting Evaluation in the Wild
Wanli Yang, Fei Sun, Jiajun Tan +5
Despite near-perfect results reported in the literature, the effectiveness of model editing in real-world applications remains unclear. To bridge this gap, we introduce QAEdit, a n…
MAIR: A Massive Benchmark for Evaluating Instructed Retrieval
Weiwei Sun, Zhengliang Shi, Jiulong Wu +6
Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially…
Understanding the Collapse of LLMs in Model Editing
Wanli Yang, Fei Sun, Jiajun Tan +4
Despite significant progress in model editing methods, their application in real-world scenarios remains challenging as they often cause large language models (LLMs) to collapse. A…
The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse
Wanli Yang, Fei Sun, Xinyu Ma +3
Although model editing has shown promise in revising knowledge in Large Language Models (LLMs), its impact on the inherent capabilities of LLMs is often overlooked. In this work, w…