标题： 分散的健康数据在大型语言模型中：在亚裔美国人代表性背景下评估数据公平性 显示英文标题

标题： Disaggregated Health Data in LLMs: Evaluating Data Equity in the Context of Asian American Representation

摘要： 大型语言模型（LLMs），如ChatGPT和Claude，已成为信息检索的重要工具，通常作为传统搜索引擎的替代品。 然而，确保这些模型为不同的人口群体提供准确且公平的信息仍然是一个重要的挑战。 本研究调查了LLMs在获取亚裔美国人人口中次民族群体（如韩国和华人社区）的细分健康相关信息方面的能力。 数据细分在健康研究中是一种关键实践，用于解决不平等问题，因此成为评估LLM输出中代表公平性的理想领域。 我们应用了一系列统计和机器学习工具来评估LLMs是否提供适当细分和公平的信息。 通过关注亚裔美国次民族群体，这是一个在传统分析中常被聚合的多样化人口；我们突显了LLMs如何处理健康数据中的复杂差异。 我们的研究结果有助于关于负责任人工智能的持续讨论，特别是在确保基于LLM系统的输出中的数据公平性方面。 显示英文摘要

摘要： Large language models (LLMs), such as ChatGPT and Claude, have emerged as essential tools for information retrieval, often serving as alternatives to traditional search engines. However, ensuring that these models provide accurate and equitable information tailored to diverse demographic groups remains an important challenge. This study investigates the capability of LLMs to retrieve disaggregated health-related information for sub-ethnic groups within the Asian American population, such as Korean and Chinese communities. Data disaggregation has been a critical practice in health research to address inequities, making it an ideal domain for evaluating representation equity in LLM outputs. We apply a suite of statistical and machine learning tools to assess whether LLMs deliver appropriately disaggregated and equitable information. By focusing on Asian American sub-ethnic groups, a highly diverse population often aggregated in traditional analyses; we highlight how LLMs handle complex disparities in health data. Our findings contribute to ongoing discussions about responsible AI, particularly in ensuring data equity in the outputs of LLM-based systems.