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arXiv Computation and Language By Syed Mahbubul Huq, Christopher Child, Tillman Weyde, Pranava Madhyastha

Knowing Before Answering: Decoding Language Models for Reliable RAG

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The paper introduces a method for determining whether retrieval-augmented generation (RAG) systems have sufficient, insufficient, or conflicting evidence to answer a question. By training a lightweight linear classifier on hidden activations and attention-derived features from 16 language models, the authors demonstrate that these internal signals reliably predict the adequacy of retrieved documents, outperforming prompting-based baselines and specialized RAG models. Analysis shows that middle-layer hidden states carry the most informative signals for this triage task.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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