FA ct S core: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (2023)

Abstract: Evaluating the factuality of long-form text generated by large language models (LMs) is non-trivial 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 retrieval-augmented PerplexityAI—and report new analysis demonstrating the need for such a fine-grained 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. FACTSCORE is available for public use via ‘pip install factscore‘.

## introduction long-form text generated by large language models (lms) has widely been used @xcite ; nonetheless, evaluating their factual precision-whether each piece of information conveyed in a generation is factually accurateremains challenging for two reasons. first, a generation consists of a large number of pieces of information that are a mixture of true or false, foot_0 making a binary judgment inadequate @xcite . second, validating every piece of information is time-consuming and costly. in this paper, we introduce factscore (factual precision in atomicity score), a new evaluation of an lm that represents the percentage of atomic facts (pieces of information) supported by a given knowledge source. computing factscore involves (1) breaking a generation into a series of atomic facts-short statements that each contain one piece of information @xcite @xcite , and (2) assigning a binary label to each atomic fact, allowing a fine-grained evaluation of factual precision. we evaluate factscore on the task of generating people biographies because generations consist of verifiable statements rather than debatable or subjective ones, and the scope is broad (i.e., covering diverse nationalities, professions, and levels of rarity). we perform extensive human annotations to obtain factscores of three state-of-the-art, commercially available lms: instructgpt @xcite , @xcite , and searchaugmented perplexityai. 3 our results indicate that commercially available lms are riddled with errors, having factscores of 42%, 58% and 71%, respectively. their factscores significantly drop as the rarity of the entities increases, e.g., 80% → 16% for chatgpt. since human evaluation is costly, we next introduce an automatic evaluation of factscore through a model that estimates a factscore for a given lm. our estimator decomposes generations into atomic facts and validates each based on a given knowledge source, leveraging retrieval from the given knowledge source and strong language models. our estimator closely approximates factscore with an error rate @xmath1. our case study evaluates 6,500 generations from 13 lms that could have cost @xmath04 per generation) @xcite because validating every atomic fact against a large knowledge source is time-consuming, and one generation contains many (26-41) atomic facts. this prevents lm developers and practitioners from evaluating the factual precision in long-form generation of a new lm subj at scale. in this context, we introduce a model that estimates factscore. this estimator takes a set of generations and automatically computes a factscore, and can be applied to any lm subj . we describe our model (section 4.1) and demonstrate its accuracy against human evaluation (sec-tion 4.2). factscore estimated by our model is then used to evaluate twelve lms (section 4.3). ## conclusion and future work we introduced factscore, a new evaluation of the factual precision of long-form generation from lms that breaks a generation down into a series of atomic facts and computes a fraction of facts supported by a given knowledge source. we first performed extensive human evaluation, finding that commercial, state-the-art-art lms-instructgpt, chatgpt, and search engine augmented, perplexityai-make a substantial amount of errors, e.g., having a factscore of 58% in the case of chatgpt. since human evaluation is time-consuming and costly, we proposed a model that estimates factscore, allowing an automatic evaluation of factscore. we found our estimator based on retrieval over a knowledge source and competitive language models estimates factscore close to the ground truth, and showcased its application by evaluating 12 recentlyreleased lms that could have cost $65k if evaluated by humans and providing insights about them. within four months since its initial release, factscore has actively been used in subsequent work, evaluating factual precision of recentlyproposed models @xcite @xcite . as future work, we suggest: (1) considering other aspects of factuality such as recall (coverage of factual information); (2) further improving the estimator for a better approximation of factual precision; and (3) leveraging factscore to correct model generations (briefly explored in appendix c).