Can LLM Rerankers Predict Their Own Ranking Performance?
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 training-free and training-based approaches. For training-free estimation, we examine metric-specific self-consistency across sampled rankings and verbalized confidence produced directly by the reranker. Experiments on TREC Deep Learning 2019--2022 wit...
| Authors | Shiyu Ni, Keping Bi, Jiafeng Guo et al. |
|---|---|
| Primary category | cs.IR |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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