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Sieve and Sage: Efficient Distraction Filtering for Reliable RALM Abstention
Author1, Author2, Author3, Author4, Author5
RAGabstentionefficiencyfiltering
2609.35794
Builder Relevance
1h ago80%
Abstract
The paper presents a framework that improves the reliability of Retrieval-Augmented Language Models by efficiently filtering distractions before generating responses.
Reality Card
Core Claim
The Sieve and Sage framework improves system accuracy by up to 69.4 percentage points and Macro-F1 by 55.2 percentage points while achieving a 1.99x speedup.
Method / Result
Improved accuracy by up to 69.4 percentage points.
Limitations
The paper does not detail the specific datasets or environments used for evaluation, which may affect reproducibility.
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