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arXiv · 2310.10627

Factored Verification: Detecting and Reducing Hallucination in Summaries of Academic Papers

Charlie George·Andreas Stuhlmüller
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Abstract

Hallucination plagues even frontier LLMs--but how bad is it really for summarizing academic papers? We evaluate Factored Verification, a simple automated method for detecting hallucinations in abstractive summaries. This method sets a new SotA on hallucination detection in the summarization task of the HaluEval benchmark, achieving 76.2% accuracy. We then use this method to estimate how often language models hallucinate when summarizing across multiple academic papers and find 0.62 hallucinations in the average ChatGPT (16k) summary, 0.84 for GPT-4, and 1.55 for Claude 2. We ask models to self-correct using Factored Critiques and find that this lowers the number of hallucinations to 0.49 for ChatGPT, 0.46 for GPT-4, and 0.95 for Claude 2. The hallucinations we find are often subtle, so we advise caution when using models to synthesize academic papers.

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Charlie George, Andreas Stuhlmüller. 2023-10-16. Factored Verification: Detecting and Reducing Hallucination in Summaries of Academic Papers. https://arxiv.org/abs/2310.10627

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