V eri S core: Evaluating the factuality of verifiable claims in long-form text generation (2024)

Abstract: Existing metrics for evaluating the factuality of long-form text, such as FACTSCORE (Min et al., 2023) and SAFE (Wei et al., 2024), decompose an input text into “atomic claims” and verify each against a knowledge base like Wikipedia. These metrics are not suitable for most generation tasks because they assume that every claim is verifiable (i.e., can plausibly be proven true or false). We address this issue with VERISCORE,1 a metric for evaluating factuality in diverse long-form generation tasks that contain both verifiable and unverifiable content. VERISCORE can be effectively implemented with either closed or fine-tuned open-weight language models. Human evaluation confirms that VERISCORE’s extracted claims are more sensible than those from competing methods across eight different long-form tasks. We use VERISCORE to evaluate generations from 16 different models across multiple long-form tasks and find that while GPT-4o is the best-performing model overall, open-weight models such as Mixtral-8×22 are closing the gap. We show that an LM’s VERISCORE on one task (e.g., biography generation) does not necessarily correlate to its VERISCORE on a different task (e.g., long-form QA), highlighting the need for expanding factuality evaluation across tasks with varying fact density.

## introduction modern approaches for evaluating the factuality of llm-generated long-form text, such as factscore (min et al., 2023) and safe (wei et al., 2024), proceed in three stages: (1) decomposition of the text into a list of "atomic" (i.e., short) claims; (2) retrieval of relevant evidence for each claim from wikipedia or google search; and (3) verification of each claim against the retrieved evidence. these approaches implicitly assume that the input text can be entirely decomposed into atomic and verifiable claims. unfortunately, these assumptions do not always apply to complex generation tasks such as longform question answering (lfqa) for two reasons. first, outputs for the biography generation task studied in factscore rarely go beyond introducing entities and events. however, in tasks like lfqa, we observe more complex assertions that cannot be made "atomic" without losing critical context, as in: second, many long-form outputs interleave factual claims with unverifiable content, such as betacyanin is like a superhero cape in figure 1 . since factscore and safe assume all claims are verifiable, they extract everything from the text (including unverifiable content like examples or hypotheticals), which can unfairly penalize models during the final aggregation process. as such, these metrics are limited to fact-dense and formulaic text (e.g., biographies). we address these issues by veriscore, an automatic metric that assesses models' factuality against google search results. veriscore's decomposition and verification steps are initially implemented using few-shot prompting. extensive human studies confirm the quality of both steps. subsequently, open-weight llms are fine-tuned on data generated by gpt-4 foot_1 and gpt-4o to create a cost-efficient and reliable implementation of claim extraction and verification. compared to factscore and safe, veriscore introduces three key improvements. first, veriscore is the first approach that considers inter-sentence context when extracting claims (section 2.1), removing the need for expensive claim revision steps present in safe. second, veriscore only extracts what we term verifiable claims (section 2.1.2), unlike factscore and safe which decompose everything. in a human study (section 3.1), our extraction method is preferred 93% of the time over safe's, even in biography generation. third, unlike safe that uses an llm to iteratively issue search queries for retrieving verification evidence, veriscore uses extracted claims per se as search queries (section 2.2), which is shown to be sufficient for retrieving evidence (appendix e.3). to benchmark models with veriscore, we gather prompts from eight diverse domains that require long-form responses, ranging from the factdense biography generation task of factscore to the multi-task, open-domain sharegpt for instruction-following. we evaluate sixteen closed and open-weight lms with veriscore and find that gpt-4o generates the most factually-supported text when averaged across all datasets. our analyses highlight that (1) multiple tasks (not just biography generation) are needed for comprehensive longform factuality evaluation because an llm's factuality varies depending on the task and domain; and ## veriscore: an automatic factuality metric this section details the veriscore pipeline, covering claim extraction, evidence retrieval, claim verification, and score calculation. ## using veriscore to benchmark lm factuality in this section, we use veriscore to benchmark 16 lms on 6 long-form fact-seeking domains. @xcite we first introduce our fine-tuned claim extraction and verification models that are used for our largescale study. the results of our study highlights the gap between closed and open-weight lms, where gpt-4o achieving significantly higher veriscore than any open lm. we also note tasks whose veriscore does not correlate well, and conclude with qualitative analysis revealing limitations of veriscore's verification step. ## conclusion and future work we propose veriscore, a factuality metric that focuses exclusively on verifiable claims. human evaluations validate that veriscore is more effective than existing metrics for diverse long-form generation tasks that contain both verifiable and unverifiable content. we open-source both a closedand open-weight implementation of veriscore, with the latter's performance approaching that of the former. finally, we notice that complex claims (e.g., not entity-centric or formulaic) are challenging to verify against search results. we hope that future work will improve on this aspect to develop more robust factuality metrics.