Pinhuan Wang

REALM: Recursive Relevance Modeling for LLM-based Document Re-Ranking

Large Language Models (LLMs) have shown strong capabilities in document re-ranking, a key component in modern Information Retrieval (IR) systems. However, existing LLM-based approaches face notable limitations, including ranking uncertainty, unstable top- k recovery, and high token cost due to token