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Sieve and Sage: Efficient Distraction Filtering for Reliable RALM Abstention

This story is from 2026-09-30. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.35794v1 Announce Type: new Abstract: Just as Socrates recognized the limits of his own knowledge, Retrieval-Augmented Language Models (RALMs) should learn to abstain when the retrieved evidence cannot support a reliable response. Existing approaches largely rely on monolithic LLMs to han…

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  1. 2026-09-30 04:00 · arXiv cs.CL
    Sieve and Sage: Efficient Distraction Filtering for Reliable RALM Abstention

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