A Scalable Framework for Evaluating Health Language Models

• URL: http://arxiv.org/abs/2503.23339v2
• Date: Tue, 01 Apr 2025 21:17:55 GMT
• Title: A Scalable Framework for Evaluating Health Language Models
• Authors: Neil Mallinar, A. Ali Heydari, Xin Liu, Anthony Z. Faranesh, Brent Winslow, Nova Hammerquist, Benjamin Graef, Cathy Speed, Mark Malhotra, Shwetak Patel, Javier L. Prieto, Daniel McDuff, Ahmed A. Metwally,
• Abstract summary: Large language models (LLMs) have emerged as powerful tools for analyzing complex datasets.<n>Current evaluation practices for open-ended text responses heavily rely on human experts.<n>This work introduces Adaptive Precise Boolean rubrics: an evaluation framework that streamlines human and automated evaluation of open-ended questions.
• Score: 16.253655494186905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have emerged as powerful tools for analyzing complex datasets. Recent studies demonstrate their potential to generate useful, personalized responses when provided with patient-specific health information that encompasses lifestyle, biomarkers, and context. As LLM-driven health applications are increasingly adopted, rigorous and efficient one-sided evaluation methodologies are crucial to ensure response quality across multiple dimensions, including accuracy, personalization and safety. Current evaluation practices for open-ended text responses heavily rely on human experts. This approach introduces human factors and is often cost-prohibitive, labor-intensive, and hinders scalability, especially in complex domains like healthcare where response assessment necessitates domain expertise and considers multifaceted patient data. In this work, we introduce Adaptive Precise Boolean rubrics: an evaluation framework that streamlines human and automated evaluation of open-ended questions by identifying gaps in model responses using a minimal set of targeted rubrics questions. Our approach is based on recent work in more general evaluation settings that contrasts a smaller set of complex evaluation targets with a larger set of more precise, granular targets answerable with simple boolean responses. We validate this approach in metabolic health, a domain encompassing diabetes, cardiovascular disease, and obesity. Our results demonstrate that Adaptive Precise Boolean rubrics yield higher inter-rater agreement among expert and non-expert human evaluators, and in automated assessments, compared to traditional Likert scales, while requiring approximately half the evaluation time of Likert-based methods. This enhanced efficiency, particularly in automated evaluation and non-expert contributions, paves the way for more extensive and cost-effective evaluation of LLMs in health.

Related papers

• Med-CoDE: Medical Critique based Disagreement Evaluation Framework [72.42301910238861]
  The reliability and accuracy of large language models (LLMs) in medical contexts remain critical concerns. Current evaluation methods often lack robustness and fail to provide a comprehensive assessment of LLM performance. We propose Med-CoDE, a specifically designed evaluation framework for medical LLMs to address these challenges.
  arXiv  Detail & Related papers  (2025-04-21T16:51:11Z)
• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references.<n>We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey.<n>Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)
• Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation [31.061600616994145]
  HDCEval is built on a set of fine-grained medical evaluation guidelines developed in collaboration with professional doctors.<n>The framework decomposes complex evaluation tasks into specialized subtasks, each evaluated by expert models.<n>This hierarchical approach ensures that each aspect of the evaluation is handled with expert precision, leading to a significant improvement in alignment with human evaluators.
  arXiv  Detail & Related papers  (2025-01-12T07:30:49Z)
• Towards Leveraging Large Language Models for Automated Medical Q&A Evaluation [2.7379431425414693]
  This paper explores the potential of using Large Language Models (LLMs) to automate the evaluation of responses in medical Question and Answer (Q&A) systems.
  arXiv  Detail & Related papers  (2024-09-03T14:38:29Z)
• Building Trust in Mental Health Chatbots: Safety Metrics and LLM-Based Evaluation Tools [13.386012271835039]
  We created an evaluation framework with 100 benchmark questions and ideal responses. This framework, validated by mental health experts, was tested on a GPT-3.5-turbo-based chatbots.
  arXiv  Detail & Related papers  (2024-08-03T19:57:49Z)
• A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Review [11.28580626017631]
  We highlight a notable need for a standardized and consistent human evaluation approach. We propose a comprehensive and practical framework for human evaluation of large language models (LLMs) This framework aims to improve the reliability, generalizability, and applicability of human evaluation of LLMs in different healthcare applications.
  arXiv  Detail & Related papers  (2024-05-04T04:16:07Z)
• A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models [20.11590976578911]
  Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developing systems that promote health equity. We present resources and methodologies for surfacing biases with potential to precipitate equity-related harms in long-form, LLM-generated answers to medical questions.
  arXiv  Detail & Related papers  (2024-03-18T17:56:37Z)
• 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.<n>Here, we explore a general mitigation framework using Attribute Structuring (AS), which structures the summary evaluation process.<n>AS consistently improves the correspondence between human annotations and automated metrics in clinical text summarization.
  arXiv  Detail & Related papers  (2024-03-01T21:59:03Z)
• Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI [38.497288024393065]
  Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. This paper explores state-of-the-art evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare.
  arXiv  Detail & Related papers  (2023-09-21T19:36:48Z)
• ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate [57.71597869337909]
  We build a multi-agent referee team called ChatEval to autonomously discuss and evaluate the quality of generated responses from different models. Our analysis shows that ChatEval transcends mere textual scoring, offering a human-mimicking evaluation process for reliable assessments.
  arXiv  Detail & Related papers  (2023-08-14T15:13:04Z)
• From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation [60.14902811624433]
  We discuss a paradigm shift from static evaluation methods to adaptive testing. This involves estimating the characteristics and value of each test item in the benchmark and dynamically adjusting items in real-time. We analyze the current approaches, advantages, and underlying reasons for adopting psychometrics in AI evaluation.
  arXiv  Detail & Related papers  (2023-06-18T09:54:33Z)
• Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach [84.02388020258141]
  We propose a new framework named ENIGMA for estimating human evaluation scores based on off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
  arXiv  Detail & Related papers  (2021-02-20T03:29:20Z)

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.