arXiv:2606.03535v1 Announce Type: cross Abstract: 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 sel
The rapid advancement of large language models (LLMs) makes exploring their emergent capabilities, such as self-assessment, a current frontier in AI research and application.
Improving the ability of LLMs to self-evaluate their ranking performance could significantly enhance the reliability and efficiency of retrieval systems, reducing the need for extensive human oversight.
LLM-powered search and retrieval systems could become more autonomous and robust, leading to faster development cycles and more accurate information delivery.
- · AI-driven search engines
- · Information retrieval companies
- · AI researchers
Retrieval systems incorporating LLM rerankers will achieve higher accuracy and reduce human intervention in quality assurance.
This capability could lead to more dynamic and personalized information retrieval experiences across various applications, from customer service to scientific discovery.
Sophisticated self-evaluating AI systems might accelerate the development of truly autonomous AI agents capable of complex, unsupervised tasks.
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Read at arXiv cs.CL