Shuai Wang

Large Language Models (LLMs) have emerged as powerful re-rankers. Recent research has however showed that simple prompt injections embedded within a candidate document (i.e., jailbreak prompt attacks) can significantly alter an LLM's ranking decisions. While this poses serious security risks to L...

Document chunking is a critical preprocessing step in dense retrieval systems, yet the design space of chunking strategies remains poorly understood. Recent research has proposed several concurrent approaches, including LLM-guided methods (e.g., DenseX and LumberChunker) and contextualized strate...

We introduce Rank-R1, an LLM-based reranker that reasons over queries and candidate documents before ranking. Existing rerankers based on LLMs rely on prompting or fine-tuning to order documents by relevance without explicit reasoning. Rank-R1 uses reinforcement learning with only relevance label...