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Factored Verification: Detecting and Reducing Hallucination in Summaries of Academic Papers
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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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Cited by 1 Pith paper
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OpenFActScore: Open-Source Atomic Evaluation of Factuality in Text Generation
Using Olmo to extract atomic facts and Gemma to verify them against Wikipedia, OpenFActScore reproduces the original FActScore ranking of 10 LLMs with a Pearson correlation above 0.99.
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