Improved Evidence Extraction for Document Inconsistency Detection with LLMs

• URL: http://arxiv.org/abs/2601.02627v1
• Date: Tue, 06 Jan 2026 00:58:20 GMT
• Title: Improved Evidence Extraction for Document Inconsistency Detection with LLMs
• Authors: Nelvin Tan, Yaowen Zhang, James Asikin Cheung, Fusheng Liu, Yu-Ching Shih, Dong Yang,
• Abstract summary: We introduce new comprehensive evidence-extraction metrics and a redact-and-retry framework with constrained filtering.<n>We back our claims with promising experimental results.
• Score: 10.610567456326235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are becoming useful in many domains due to their impressive abilities that arise from large training datasets and large model sizes. However, research on LLM-based approaches to document inconsistency detection is relatively limited. There are two key aspects of document inconsistency detection: (i) classification of whether there exists any inconsistency, and (ii) providing evidence of the inconsistent sentences. We focus on the latter, and introduce new comprehensive evidence-extraction metrics and a redact-and-retry framework with constrained filtering that substantially improves LLM-based document inconsistency detection over direct prompting. We back our claims with promising experimental results.

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