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arXiv · 2604.07985

Predicting the Benefit of Retrieval Augmentation in Open-Domain Question Answering

Or Dado·David Carmel·Oren Kurland
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Abstract

While retrieval augmented generation has become a common approach for enhancing question answering systems, retrieval is not universally advantageous. We study the problem of predicting whether incorporating external retrieved information is likely to improve response quality for a given question. To this end, we evaluate a range of prediction methods that are based on retrieval signals, answer characteristics, and semantic consistency between generated responses and retrieved passages. We further devise a predictor that probes the LLM's internal state. Its prediction performance significantly narrows the performance gap between post-generation methods which are computationally demanding and pre-generation (post-retrieval) methods. We use the prediction methods to devise a selective retrieval framework that dynamically chooses between retrieval and non-retrieval generation modes per question. Experimental results demonstrate that selectively applying retrieval augmentation yields answer quality that transcends that of using retrieval for all queries.

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Or Dado, David Carmel, Oren Kurland. 2026-08-19. Predicting the Benefit of Retrieval Augmentation in Open-Domain Question Answering. https://doi.org/10.1145/3799682.3840689

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