闪耀的故事，隐藏的挣扎：通过LLM的视角调查残疾人的表现
Shiny Stories, Hidden Struggles: Investigating the Representation of Disability Through the Lens of LLMs
AI总结：
本文研究了大型语言模型（LLMs）如何表现残疾人群体，通过模拟残疾人视角生成社交媒体帖子，并与真实残疾人的帖子进行比较，揭示LLMs对残疾人的理想化倾向和潜在的偏见。
摘要：
现代大型语言模型（LLMs）近年来因其模拟人类行为和生成反映个人身份和人口群体的文本的能力而受到广泛关注。尽管这些能力可以打开跨领域多样应用的大门，但必须检查这些模型如何代表各种目标群体，因为LLMs可能会延续和放大对历史上被边缘化社区的偏见或歧视，或者由于去偏见努力，过度纠正导致过度积极的刻板印象。这种过度补偿会理想化这些群体，忽视他们所面临的复杂性和挑战，转而采用不现实的描绘。在本文中，我们通过模拟残疾人视角生成社交媒体帖子来研究LLMs如何表现残疾人群体。这些帖子随后与真实残疾人撰写的帖子进行比较，重点是情感基调、情感倾向和代表性的词汇和主题。我们的分析揭示了两个关键发现：（1）LLMs常常理想化残疾人的经历，生成过于积极的刻板印象，尽管看起来鼓舞人心，但未能真实捕捉他们的现实生活；（2）对模拟有和没有残疾人的帖子的比较分析显示了一种负面偏见，某些主题，如职业和娱乐，被不成比例地与非残疾人群体相关联。这强化了排斥性叙述和对残疾的过度理想化描绘，歪曲了该社区实际面临的挑战。这些发现与更广泛的关注和持续研究一致，表明LLMs难以反映社会的多样现实，特别是边缘化群体的复杂经历，并强调了对其表现进行批判性审视的必要性。
英文摘要：
Modern Large Language Models (LLMs) have recently attracted much attention for their ability to simulate human behavior and generate text that reflects personas and demographic groups. While these capabilities can open up a multitude of diverse applications across fields, it is crucial to examine how such models represent various target groups since LLMs can perpetuate and amplify biases or discrimination against historically marginalized communities or, alternatively, as a result of debiasing efforts, overcorrect by portraying overly positive stereotypes. This overcompensation can idealize these groups, erasing the complexities and challenges they face in favor of unrealistic depictions. In this paper, we investigate how LLMs represent disability by simulating the perspectives of individuals with disabilities in generating social media posts. These posts are then compared with those written by real people with disabilities, focusing on emotional tone, sentiment, and representative words and themes. Our analysis reveals two key findings: (1) LLMs often idealize the experiences of people with disabilities, producing overly positive stereotypes that, despite appearing uplifting, fail to authentically capture their lived realities; and (2) a comparative analysis of posts simulating individuals with and without disabilities highlights a negative bias, where certain topics, such as career and entertainment, are disproportionately associated with nondisabled individuals. This reinforces exclusionary narratives and over-idealized portrayals of disability, misrepresenting the actual challenges faced by this community. These findings align with broader concerns and ongoing research showing that LLMs struggle to reflect the diverse realities of society, particularly the nuanced experiences of marginalized groups, and underscore the need for critical scrutiny of their representations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
