November 17, 2023
Evaluating the factuality of long-form text generated by large language models (LMs) is nontrivial because (1) generations often contain a mixture of supported and unsupported pieces of information, making binary judgments of quality inadequate, and (2) human evaluation is time-consuming and costly. In this paper, we introduce FACTSCORE, a new evaluation that breaks a generation into a series of atomic facts and computes the percentage of atomic facts supported by a reliable knowledge source. We conduct an extensive human evaluation to obtain FACTSCOREs of people biographies generated by several state-of-the-art commercial LMs—InstructGPT, ChatGPT, and the retrievalaugmented PerplexityAI—and report new analysis demonstrating the need for such a finegrained score (e.g., ChatGPT only achieves 58%). Since human evaluation is costly, we also introduce an automated model that estimates FACTSCORE using retrieval and a strong language model, with less than a 2% error rate. Finally, we use this automated metric to evaluate 6,500 generations from a new set of 13 recent LMs that would have cost $26K if evaluated by humans, with various findings: GPT-4 and ChatGPT are more factual than public models, and Vicuna and Alpaca are some of the best public models.
Written by
Mike Lewis
Hannaneh Hajishirzi
Kalpesh Krishna
Mohit Iyyer
Pang Wei Koh
Sewon Min
Xinxi Lyu
Publisher
EMNLP
Research Topics
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