Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation

Published: 16 Jan 2024, Last Modified: 15 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: language model, hallucination, trustworthy artificial intelligence, reasoning
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TL;DR: We present a comprehensive analysis showing that state-of-the-art LLMs frequently produce self-contradictory hallucinations. We then design prompting methods that effectively detect and mitigate self-contradictions.
Abstract: Large language models (large LMs) are susceptible to producing text that contains hallucinated content. An important instance of this problem is self-contradiction, where the LM generates two contradictory sentences within the same context. In this work, we present a comprehensive investigation into self-contradiction for various instruction-tuned LMs, covering evaluation, detection, and mitigation. Our primary evaluation task is open-domain text generation, but we also demonstrate the applicability of our approach to shorter question answering. Our analysis reveals the prevalence of self-contradictions, e.g., in 17.7% of all sentences produced by ChatGPT. We then propose a novel prompting-based framework designed to effectively detect and mitigate self-contradictions. Our detector achieves high accuracy, e.g., around 80% F1 score when prompting ChatGPT. The mitigation algorithm iteratively refines the generated text to remove contradictory information while preserving text fluency and informativeness. Importantly, our entire framework is applicable to black-box LMs and does not require retrieval of external knowledge. Rather, our method complements retrieval-based methods, as a large portion of self-contradictions (e.g., 35.2% for ChatGPT) cannot be verified using online text. Our approach is practically effective and has been released as a push-button tool to benefit the public at
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Primary Area: societal considerations including fairness, safety, privacy
Submission Number: 9113