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Guaranteeing Faithful Evidence Extraction in Speculative Retrieval-Augmented Generation
Large Language Models (LLMs) are increasingly used as interfaces for information retrieval, but they remain prone to hallucinations and faithfulness errors, in which the generated answers diverge from the retrieved evidence. While Retrieval-Augmented Generation (RAG) and recent hybrid or semi-extrac
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