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MedSNIP: Building and Benchmarking Snippet-Level Granularity for Medical Fact Verification

Paper recorded by Signals 4 on 2026-09-11 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-11 on arXiv · recorded by Signals 4 on 2026-09-14

Category: cs.CL · 自然语言处理 · first seen 2026-09-14

Abstract

A medical claim's correctness often depends not on the claim alone, but on the clinical structure around it. A claim may require a lab reference range, a causal or conditional link, or patient-specific details to be judged correctly, and atom-level decomposition can fragment these dependencies, leaving the verifier with clinically incomplete claims. We reformulate medical fact-checking around snip

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#60 most recent of 186 cs.CL papers we have recorded · ↑ newer: Parameter-Efficient Retrievers for Polish and European Languages · ↓ older: DuplexDrama: A Synthesized Dialogue Dataset with Scenarios, Full-Duple
Cite this page: MedSNIP: Building and Benchmarking Snippet-Level Granularity for Medical Fact Verification: the #60 most recent of 186 cs.CL papers we have recorded (as of 2026-09-11). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/medsnip-building-and-benchmarking-snippet-level-granularity-for-medical-fact-ver.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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