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arXiv · 2512.18601

On Finding Inconsistencies in Documents

Charles J. Lovering·Seth Ebner·Brandon Smock·Michael Krumdick·Saad Rabbani·Ahmed Muhammad·Varshini Reddy·Chris Tanner
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Abstract

Professionals in academia, law, and finance audit their documents because inconsistencies can result in monetary, reputational, and scientific costs. Language models (LMs) have the potential to dramatically speed up this auditing process. To understand their abilities, we introduce a benchmark, FIND (Finding INconsistencies in Documents), where each example is a document with an inconsistency inserted manually by a domain expert. Despite the documents being long, technical, and complex, the best-performing model (gpt-5) recovered 64% of the inserted inconsistencies. Surprisingly, gpt-5 also found undiscovered inconsistencies present in the original documents. For example, on 50 arXiv papers, we judged 136 out of 196 of the model's suggestions to be legitimate inconsistencies missed by the original authors. However, despite these findings, even the best models miss almost half of the inconsistencies in FIND, demonstrating that inconsistency detection is still a challenging task.

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Charles J. Lovering, Seth Ebner, Brandon Smock, Michael Krumdick, Saad Rabbani, Ahmed Muhammad, Varshini Reddy, Chris Tanner. 2025-12-21. On Finding Inconsistencies in Documents. https://arxiv.org/abs/2512.18601

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