AI RESEARCH

Can LLM Rerankers Predict Their Own Ranking Performance?

arXiv CS.CL

ArXi:2606.03535v1 Announce Type: cross Retrieval effectiveness varies substantially across queries, making it important to estimate ranking quality before relevance judgments are available. Query performance prediction (QPP) addresses this need, but most existing methods rely on external predictors after retrieval or reranking. In this paper, we study \textit{reranker-internal QPP}: can an LLM reranker estimate the quality of the ranking it has just produced? We investigate both