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GANDR: Claim Auditing for Verifiable Legal Answer Generation

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

Published 2026-09-09 on arXiv · recorded by Signals 4 on 2026-09-10

Category: cs.AI · 人工智能 · first seen 2026-09-10

Abstract

In high-stakes domains such as legal practice, a language-model answer is only useful to the extent that a reader can verify each claim against the source the system cites. Current grounded-generation pipelines score the answer as a whole, so a correct conclusion can rest on fabricated or loosely matched citations and still score well. Closing this gap requires both a system built for per-claim ve

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#140 most recent of 300 cs.AI papers we have recorded · ↑ newer: Learning Intrusion Response Strategies for OT Systems · ↓ older: TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body
Cite this page: GANDR: Claim Auditing for Verifiable Legal Answer Generation: the #140 most recent of 300 cs.AI papers we have recorded (as of 2026-09-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/gandr-claim-auditing-for-verifiable-legal-answer-generation.html
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