AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering
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. With a growing share of question answering systems built on top of commercial language model APIs, a method that can decide how much to retrieve, and how far to trust its own answers, without retraining the underlying model, is of clear practical value. This paper presents AB-RAG (Adaptive Budgeted Retrieval-Augmented Generation), a training-free ...
| Authors | Ansh Kamthan |
|---|---|
| Primary category | cs.CL |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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