F act PICO : Factuality Evaluation for Plain Language Summarization of Medical Evidence (2024)

Abstract: Plain language summarization with LLMs can be useful for improving textual accessibility of technical content. But how factual are these summaries in a high-stakes domain like medicine? This paper presents FactPICO, a factuality benchmark for plain language summarization of medical texts describing randomized controlled trials (RCTs), which are the basis of evidence-based medicine and can directly inform patient treatment. FactPICO consists of 345 plain language summaries of RCT abstracts generated from three LLMs (i.e., GPT-4, Llama-2, and Alpaca), with fine-grained evaluation and natural language rationales from experts. We assess the factuality of critical elements of RCTs in those summaries: Populations, Interventions, Comparators, Outcomes (PICO), as well as the reported findings concerning these. We also evaluate the correctness of the extra information (e.g., explanations) added by LLMs. Using FactPICO, we benchmark a range of existing factuality metrics, including the newly devised ones based on LLMs. We find that plain language summarization of medical evidence is still challenging, especially when balancing between simplicity and factuality, and that existing metrics correlate poorly with expert judgments on the instance level.

## introduction new findings in medicine observed in randomized controlled trials (rcts) are published in journal articles which describe their design and outcomes. these rcts "measure the effectiveness of a new intervention or treatment" @xcite and are the important basis of evidencebased medicine @xcite . however, * equal contribution understanding such articles requires "specific attention outside of general literacy capacities" (ad hoc committee on health literacy, 1999), rendering them effectively inaccessible to most (lay) people. ideally, healthcare providers would stay current on all medical evidence and share relevant findings with patients, but this is impractical due to the volume and growth of the evidence base @xcite . llms may provide a means for lay readers to access such findings by automatically summarizing and simplifying texts into plain language @xcite . done successfully, this could allow patients to access the most up-todate literature relevant to their healthcare. in turn, this may promote health literacy broadly by disseminating trustworthy information @xcite . but given the inherent risks to personal health, the factual correctness of such outputs is paramount in this domain. while @xcite showed that gpt-3 infrequently introduced outright errors when simplifying rct abstracts, inaccuracies are occasionally introduced; this ought to be addressed before wide adoption of such technology. unfortunately, there is no standard evaluation benchmark for factuality on this important medical evidence text simplification task. consequently, it is unknown whether and to what degree existing automatic factuality evaluation metrics align with human judgments. we posit that focusing on critical elements in the rct structure is key for factual medical evidence communication. this work presents fact-pico, an expert-constructed factuality benchmark for the plain language summarization of technical abstracts describing rcts. foot_0 factpico is a finegrained benchmark focused on key characteristics of trials: populations (e.g. covid patients; diabetics), interventions (e.g. remdesivir), comparators (e.g. placebo), outcomes (e.g. 30 day mortality, or pain), as well as evidence inference (i.e., whether the intervention yielded a significant difference in the treated group with respect to the outcome; @xcite . pico is a standard framework to structure clinical questions. figure 1 shows an example of factpico annotation. in contrast to standard summaries, plain language summaries have the additional goal of simplifying content for lay readers. this may involve elaboration and explanation of difficult concepts to foster understanding @xcite . thus, factpico includes a correctness assessment of added content. factpico is also distinct in that it includes expert-written rationales that contextualize the evaluation of these fine-grained characteristics, providing a useful first step in assessing explainable factuality evaluation methods. factpico includes outputs from a mix of proprietary and open-source llms (gpt-4, llama-2-chat, and alpaca). our findings are somewhat less optimistic than prior work in medical @xcite and news summarization @xcite : factual errors (occasionally important ones) with respect to key rct elements are introduced by llms. one concerning phenomenon is the extent to which models overgeneralize, resulting in problematic information loss @xcite . using factpico, we evaluated a suite of exist-ing automatic metrics shown to perform well for factuality in summarization @xcite @xcite @xcite , as well as newly devised llm-based evaluations. we find that existing metrics correlate with expert ratings at the system level, but not at the instance level. the best performing metric is an llm-based approach in which we first identify key rct elements; this shows that providing models with explicit domain knowledge may help. analysis of llm-generated rationales shows that llms often provide flawed reasoning when justifying their self-evaluations. ## factpico benchmark the factpico benchmark consists of expert factuality assessments of 345 llm-generated plain language summaries of 115 rct abstracts. ## evaluation and analysis of plain language summaries results of the factuality evaluation of the plain language summaries generated by gpt-4, llama-2, and alpaca in factpico are presented in we report average ratings for each pico category and evidence inference, as well as the number and percentage of non-factual added information spans identified by either of the evaluators. since readability is a key goal of plain language summarization, we also report the change in flesch-kincaid grade level @xcite between the abstract and the summary (∆fk), as well as the rouge-l score @xcite , which quantifies ngram overlap between the generated plain language summary and the original abstract (i.e., high overlap may indicate low readability). factuality vs. simplicity. we observe a clear trade-off between the simplicity of the generated text and its factuality. plain language summaries generated by alpaca are rated as more factual across all dimensions, including most of the automatic metrics ( can llms reliably convey critical rct elements? the fine-grained framework in fact-pico exposes issues with llm-generated plain language summaries for medical texts. as shown in scientists did a study to see if people who know they have a gene that puts them at risk for heart problems would eat healthier. they looked at people's intake of a vitamin called folate, which is good for heart health. the participants in the study were told if they have the risk gene or not. but in the end, there was no difference in how much folate the people with the risk gene ate compared to those without the risk gene. so, knowing if they have the risk gene didn't make people eat healthier. in the original rct abstract, participants received "either general healthy eating advice or varying lev-supplementary elaboration (factual) source: we conducted a randomized, placebo-controlled clinical trial to prove efficacy of botulinum toxin for neck pain in chronic whiplash syndrome. simplification [gpt-4]: scientists did a study to see if a certain type of medicine, called botulinum toxin, could help people who had long-lasting neck pain from whiplash (an injury that happens if your head jerks hard). rationale: this is an explanation that is not mentioned in the original. ## factuality evaluation metrics factpico is a dataset that can be used to assess automatic evaluation methods for plain language summarization of rct texts. we assess existing methods used for factuality evaluation, as well as the capabilities of llms themselves to evaluate factuality. these analyses focus on questions about pico elements and evidence inference; we leave evaluation of the factuality of added information for future work, as this entails fact-checking using external knowledge sources. ## factuality metric evaluation results to conduct a meta evaluation of the system factuality metrics, we compute the kendall's τ b coefficient, spearman correlation coefficient, and pairwise accuracy coefficient @xcite for automatic vs. human evaluations for each of the pico and evidence inference aspect. we posit that good metrics must correlate well with the most salient elements in a high-stake domain. instance-level results. we present the results for the above metrics in factpico ratings correlated more with dedicated factuality models than llm evaluators with the exception of gpt-4. in fact, the smaller, opensource llms had barely any correlation at all with the factpico ratings. these results show that there is a sizeable gap in an llms ability to evaluate generated text compared to its generation capabilities. system-level results. llms in ## preliminary llm rationale analysis we perform a preliminary analysis on llm rationales, comparing to expert rationales in fact-pico; we leave a thorough human evaluation of llm rationales for future work. qualitative analysis of a small sample of rationales show that llama-2 and mistral are often able to comprehend the medical text but fail in making correct judgments according to the provided instructions. most commonly, mistral focuses on the abstract rather than the evaluated summary, while llama-2 generates long explanations that eventually arrive at the wrong conclusion. rationales from gpt-4 and its pipelined counterpart usually did not do this. for the most part, rationales made logical sense. however, some rationales were overly generous in it evaluation, ignoring critical errors. we provide examples of such erroneous rationales in appendix m. ## related work meta-evaluation of factual consistency metrics in summarization (and related tasks) in the "general domain", e.g., wikipedia and news, has garnered considerable attention @xcite @xcite @xcite . however, in addition to focusing on different domains, these existing benchmarks in summarization include primarily older models. newer llms may yield novel error types (or be more factual overall) @xcite . existing summarization factuality benchmarks also fail to generalize to simplification, in which content addition in the form of elaborations or explanations is often necessary @xcite . broadly, simplification entails substantial language changes that often lead to the text being more general @xcite . @xcite evaluated the factuality of automated simplification model outputs at the sentence-level, noticing that content deletion can often lead to factual errors (in contrast to only summarization). our findings confirm that these errors also exist in plain language summarization; the overgeneralization problem may lead to safety issues in the medical domain. @xcite and @xcite evaluated llm-generated summaries of medical evidence. notably, @xcite 's work included an evaluation of plain language summaries. our work deepens this analysis with a finer-grained evaluation focusing on critical components of rcts and medical evidence, covering three llms. our findings call for caution against llm-generated plain language summaries despite the absence of outright inconsistencies. @xcite presents an analysis of "hallucinations" in medical qa tests, focusing on reasoning rather than factual consistency. the inclusion of human-written natural language rationales in factuality benchmarks is rare, and there is a paucity of work evaluating these. the felm benchmark @xcite , an opendomain evaluation of llm-generated long form texts covering factual knowledge, math, and reasoning included human rationales. work in llm critiquing has started to incorporate natural language critiques from both humans @xcite and llms @xcite . factpico is the first factuality benchmark of the plain language summarization task that includes expert-generated natural language rationales. ## conclusions we introduced factpico, an expert-annotated benchmark in the domain of evidence-based medicine for evaluating the factuality of plain language summarization with respect to clinically important dimensions. using factpico, we presented an analysis of factual errors along these fine-grained aspects in llm-generated plain language summaries. we also presented an analysis of methods to evaluate factuality, including both dedicated factuality models and novel llm-based methods.