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arXiv AI By Owen Walters, Lihu Chen, Francesca Toni

Retrieved but Not Read: Understanding Why Search-Augmented LLMs Fail to Use Available Evidence

Read the original on arXiv AI →

The paper investigates why large language models (LLMs) sometimes give outdated answers even when recent evidence is available through web search. By creating temporal facts that changed after the models’ training cutoffs, the authors evaluate a proprietary model with deployed search and six open-weight models with controlled retrieval. They find that models often rely on search snippets without opening source pages, leading to more outdated answers and unfaithful citations, and that even when retrieved evidence influences internal predictions, the final answer may still favor outdated information.

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 AI.

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