Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation. We investigate whether verbalized confidence from black-box language models can serve as an actionable signal for retrieval routing.
arXiv:2606. 29090v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty.
By Ansh Kamthan
Retrieval-Augmented Generation (RAG) has become the standard way to ground large language models in external knowledge, yet most systems retrieve a fixed number of passages for every question regardless of its difficulty. This wastes computation on easy questions, starves hard ones, and gives no signal for when a generated answer can be trusted.
The paper introduces matched trajectory replay, a protocol that fixes answer states, evidence points, budgets, and action costs to evaluate how confidence signals influence agent actions. Using this method, the authors compare raw verbalized confidence with post‑hoc isotonic calibration across six model‑dataset pairs, finding that calibration can significantly improve accuracy of committed answers but may reduce coverage and increase retrieval usage. The study concludes that calibration helps interpret commitment risk but does not predict the benefit of additional retrieval, indicating the need for separate value‑of‑information estimates.
By Prateek Chhikara
The paper evaluates how modern large language models use internal web search to answer factual questions. Using 783 static queries and 288 dynamic queries, the authors find that enabling retrieval improves accuracy on static questions but hurts confidence calibration. On dynamic queries, models often retrieve but still achieve less than 70% accuracy, mainly due to poor query formulation and source selection, indicating that internal web search works better as a quick verification tool than a full information‑retrieval system.
By Sahil Kale
ConfRAG introduces a confidence-guided approach to reduce hallucinations in large language models and selectively trigger Retrieval-Augmented Generation (RAG) only when the model is uncertain. The ConfQA fine‑tuning strategy trains the model to answer correctly or respond with "I am unsure," achieving a drop in hallucination rates from 20‑40% to below 5% across factuality benchmarks. Building on ConfQA, ConfRAG limits external retrievals by more than 30% while maintaining over 95% accuracy in ideal scenarios.
By Yin Huang, Yifan Ethan Xu, Kai Sun, Vera Yan, Alicia Sun, Haidar Khan, Jimmy Nguyen, Jingxiang Chen, Mohammad Kachuee, Zhaojiang Lin, Yue Liu, Aaron Colak, Anuj Kumar, Wen-tau Yih, Xin Luna Dong