Assessing the Limitations of Large Language Models in Clinical Fact Decomposition

• URL: http://arxiv.org/abs/2412.12422v1
• Date: Tue, 17 Dec 2024 00:07:05 GMT
• Title: Assessing the Limitations of Large Language Models in Clinical Fact Decomposition
• Authors: Monica Munnangi, Akshay Swaminathan, Jason Alan Fries, Jenelle Jindal, Sanjana Narayanan, Ivan Lopez, Lucia Tu, Philip Chung, Jesutofunmi A. Omiye, Mehr Kashyap, Nigam Shah,
• Abstract summary: We present FactEHR, a dataset consisting of full document fact decompositions for 2,168 clinical notes spanning four types from three hospital systems. Our evaluation, including review by clinicians, highlights significant variability in the quality of fact decomposition for four commonly used LLMs. Results underscore the need for better LLM capabilities to support factual verification in clinical text.
• Score: 3.919419934122265
• License:
• Abstract: Verifying factual claims is critical for using large language models (LLMs) in healthcare. Recent work has proposed fact decomposition, which uses LLMs to rewrite source text into concise sentences conveying a single piece of information, as an approach for fine-grained fact verification. Clinical documentation poses unique challenges for fact decomposition due to dense terminology and diverse note types. To explore these challenges, we present FactEHR, a dataset consisting of full document fact decompositions for 2,168 clinical notes spanning four types from three hospital systems. Our evaluation, including review by clinicians, highlights significant variability in the quality of fact decomposition for four commonly used LLMs, with some LLMs generating 2.6x more facts per sentence than others. The results underscore the need for better LLM capabilities to support factual verification in clinical text. To facilitate future research in this direction, we plan to release our code at \url{https://github.com/som-shahlab/factehr}.

Related papers

• Fact or Guesswork? Evaluating Large Language Model's Medical Knowledge with Structured One-Hop Judgment [108.55277188617035]
  Large language models (LLMs) have been widely adopted in various downstream task domains, but their ability to directly recall and apply factual medical knowledge remains under-explored. Most existing medical QA benchmarks assess complex reasoning or multi-hop inference, making it difficult to isolate LLMs' inherent medical knowledge from their reasoning capabilities. We introduce the Medical Knowledge Judgment, a dataset specifically designed to measure LLMs' one-hop factual medical knowledge.
  arXiv  Detail & Related papers  (2025-02-20T05:27:51Z)
• Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-Oasis [78.07225438556203]
  We introduce LLM-Oasis, the largest resource for training end-to-end factuality evaluators. It is constructed by extracting claims from Wikipedia, falsifying a subset of these claims, and generating pairs of factual and unfactual texts. We then rely on human annotators to both validate the quality of our dataset and to create a gold standard test set for factuality evaluation systems.
  arXiv  Detail & Related papers  (2024-11-29T12:21:15Z)
• Edinburgh Clinical NLP at MEDIQA-CORR 2024: Guiding Large Language Models with Hints [8.547853819087043]
  We evaluate the capability of general LLMs to identify and correct medical errors with multiple prompting strategies. We propose incorporating error-span predictions from a smaller, fine-tuned model in two ways. Our best-performing solution with 8-shot + CoT + hints ranked sixth in the shared task leaderboard.
  arXiv  Detail & Related papers  (2024-05-28T10:20:29Z)
• Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries [56.31117605097345]
  Large language models (LLMs) have shown the potential to generate accurate clinical text summaries, but still struggle with issues regarding grounding and evaluation. Here, we explore a general mitigation framework using Attribute Structuring (AS), which structures the summary evaluation process. AS consistently improves the correspondence between human annotations and automated metrics in clinical text summarization.
  arXiv  Detail & Related papers  (2024-03-01T21:59:03Z)
• From RAGs to riches: Using large language models to write documents for clinical trials [0.0]
  Large language models (LLMs) offer the potential to rapidly generate first versions of clinical trial documents. We report an evaluation of LLMs in generating parts of one such document, clinical trial protocols. To improve performance, we used retrieval-augmented generation (RAG) to prompt an LLM with accurate up-to-date information.
  arXiv  Detail & Related papers  (2024-02-26T08:59:05Z)
• UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models [73.73303148524398]
  Large language models (LLMs) may generate text that lacks consistency with human knowledge, leading to factual inaccuracies or textithallucination. We propose textttUFO, an LLM-based unified and flexible evaluation framework to verify facts against plug-and-play fact sources.
  arXiv  Detail & Related papers  (2024-02-22T16:45:32Z)
• FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence [46.71469172542448]
  This paper presents FactPICO, a factuality benchmark for plain language summarization of medical texts. It consists of 345 plain language summaries of abstracts generated from three randomized controlled trials (RCTs) We assess the factuality of critical elements of RCTs in those summaries, as well as the reported findings concerning these.
  arXiv  Detail & Related papers  (2024-02-18T04:45:01Z)
• Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization [8.456700096020601]
  Large language models (LLMs) have shown promise in natural language processing (NLP), but their effectiveness on a diverse range of clinical summarization tasks remains unproven. In this study, we apply adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks. A clinical reader study with ten physicians evaluates summary, completeness, correctness, and conciseness; in a majority of cases, summaries from our best adapted LLMs are either equivalent (45%) or superior (36%) compared to summaries from medical experts.
  arXiv  Detail & Related papers  (2023-09-14T05:15:01Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.