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        <title>ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models</title>
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    <abstract>In recent times, large language models (LLMs) have shown impressive performance on various document-level tasks such as document classification, summarization, and question-answering. However, research on understanding their capabilities on the task of self-contradictions in long documents has been very limited. In this work, we introduce ContraDoc, the first human-annotated dataset to study self-contradictions in long documents across multiple domains, varying document lengths, self-contradiction types, and appearance scope. We then analyze the current capabilities of four state-of-the-art open-source and commercially available LLMs: GPT3.5, GPT4, PaLM2, and LLaMAv2 on this dataset. While GPT4 performs the best and can outperform humans on this task, we find that it is still unreliable and struggles with self-contradictions that require more nuance and context. We release the dataset and all the code associated with the experiments.</abstract>
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%0 Conference Proceedings
%T ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models
%A Li, Jierui
%A Raheja, Vipul
%A Kumar, Dhruv
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F li-etal-2024-contradoc
%X In recent times, large language models (LLMs) have shown impressive performance on various document-level tasks such as document classification, summarization, and question-answering. However, research on understanding their capabilities on the task of self-contradictions in long documents has been very limited. In this work, we introduce ContraDoc, the first human-annotated dataset to study self-contradictions in long documents across multiple domains, varying document lengths, self-contradiction types, and appearance scope. We then analyze the current capabilities of four state-of-the-art open-source and commercially available LLMs: GPT3.5, GPT4, PaLM2, and LLaMAv2 on this dataset. While GPT4 performs the best and can outperform humans on this task, we find that it is still unreliable and struggles with self-contradictions that require more nuance and context. We release the dataset and all the code associated with the experiments.
%U https://aclanthology.org/2024.naacl-long.362
%P 6509-6523

Markdown (Informal)

[ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models](https://aclanthology.org/2024.naacl-long.362) (Li et al., NAACL 2024)

• ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models (Li et al., NAACL 2024)

ACL

• Jierui Li, Vipul Raheja, and Dhruv Kumar. 2024. ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 6509–6523, Mexico City, Mexico. Association for Computational Linguistics.