Know Before You Fetch: Calibrated Retrieval-Budget Allocation for Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) typically retrieves a fixed number of passages for every query. This is wasteful when the reader already knows the answer, and it can be harmful when irrelevant or partially relevant passages distract the reader. We formulate adaptive RAG as calibrated retrieval-budget allocation: given a query, decide whether to answer closed-book, retrieve a compact context (k=1), retrieve a full context (k=5), or abstain. The contribution is a probability interface rather than a new raw uncertainty signal. We calibrate sequence log-probability and prefix-logit uncertainty signals into probabilities of correctness, then use these probabilities for graded context sele...
| Authors | Zhe Dong, Fang Qin, Manish Shah et al. |
|---|---|
| Primary category | cs.IR |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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