{"version":1,"type":"story","url":"https://digestai.news/story/researchers-propose-sieve-and-sage-to-filter-ralm-distractions-for-bet","json":"https://digestai.news/story/researchers-propose-sieve-and-sage-to-filter-ralm-distractions-for-bet.json","markdown":"https://digestai.news/story/researchers-propose-sieve-and-sage-to-filter-ralm-distractions-for-bet.md","slug":"researchers-propose-sieve-and-sage-to-filter-ralm-distractions-for-bet","headline":"Researchers propose Sieve and Sage to filter RALM distractions for better abstention","summary":"A new approach called Sieve and Sage aims to improve how Retrieval-Augmented Language Models (RALMs) handle unreliable or conflicting evidence. The method splits retrieval failures into two categories: unanswerable queries (missing evidence) and distracted queries (mixed or conflicting information). Sieve, a lightweight module, first filters out distracting evidence before Sage, a more computationally expensive LLM, generates responses or decides to abstain.\n\nThe authors claim this two-step process boosts accuracy by up to 69.4 percentage points and Macro-F1 by 55.2 percentage points over one-stage baselines. It also speeds up the process by up to 1.99x. Tests were run across general and high-stakes expert domains, though no external validation is mentioned.","keyPoints":["Sieve and Sage decomposes retrieval failures into unanswerable and distracted states for better filtering","Claims 69.4% accuracy improvement and 55.2% Macro-F1 gain over existing one-stage RALM methods","Lightweight Sieve module reduces computational cost by up to 1.99x before Sage generates responses"],"whyItMatters":"If validated, this could reduce hallucinations in RALMs by cutting noisy or conflicting evidence early, lowering costs and improving trust in AI systems that rely on retrieved data.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":[],"models":["Retrieval-Augmented Language Models (RALMs)"],"people":[]},"firstPublishedAt":"2026-09-30T04:00:00Z","updatedAt":"2026-09-30T04:00:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"arXiv cs.CL","title":"Sieve and Sage: Efficient Distraction Filtering for Reliable RALM Abstention","url":"https://arxiv.org/abs/2609.35794","publishedAt":"2026-09-30T04:00:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Researchers propose Sieve and Sage to filter RALM distractions for better abstention\", 30 September 2026, https://digestai.news/story/researchers-propose-sieve-and-sage-to-filter-ralm-distractions-for-bet","publisher":"Digest AI","title":"Researchers propose Sieve and Sage to filter RALM distractions for better abstention","datePublished":"2026-09-30T04:00:00Z","url":"https://digestai.news/story/researchers-propose-sieve-and-sage-to-filter-ralm-distractions-for-bet"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}