ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models

• URL: http://arxiv.org/abs/2311.09182v2
• Date: Fri, 12 Apr 2024 21:06:43 GMT
• Title: ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models
• Authors: Jierui Li, Vipul Raheja, Dhruv Kumar,
• Abstract summary: We introduce ContraDoc, the first human-annotated dataset to study self-contradictions in long documents across multiple domains. We 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.
• Score: 7.428236410246183
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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-contradictions types, and 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 (https://github.com/ddhruvkr/CONTRADOC).

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