Shiny Stories, Hidden Struggles: Investigating the Representation of Disability Through the Lens of LLMs

光鲜的故事，隐藏的挣扎：通过大语言模型视角审视残障群体的呈现

Abstract: 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.

摘要： 近期，现代大语言模型（LLMs）因其模拟人类行为并生成反映特定人格与人口统计学特征文本的能力而备受关注。尽管这些能力为各领域开辟了多种多样的应用场景，但审视这些模型如何呈现不同目标群体至关重要。因为大语言模型可能会延续并放大针对历史上被边缘化群体的偏见或歧视；或者，作为去偏见努力的结果，模型也可能矫枉过正，描绘出过于正面的刻板印象。

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.

这种过度补偿可能会将这些群体理想化，抹杀他们所面临的复杂性和挑战，转而呈现出不切实际的形象。在本文中，我们通过模拟残障人士的视角生成社交媒体帖子，研究了大语言模型如何呈现残障议题。随后，我们将这些帖子与真实残障人士撰写的帖子进行对比，重点关注情感基调、情绪倾向以及代表性词汇和主题。

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.

我们的分析揭示了两个关键发现：（1）大语言模型往往会将残障人士的经历理想化，产生过于正面的刻板印象。尽管这些内容看起来令人振奋，却未能真实地捕捉到他们生活的现实；（2）通过对比模拟残障人士与非残障人士的帖子，我们发现了一种负面偏见：某些话题（如职业和娱乐）被不成比例地与非残障人士联系在一起。

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.

这强化了排他性的叙事和对残障群体的过度理想化描绘，歪曲了该群体所面临的实际挑战。这些发现与更广泛的担忧及当前研究相一致，即大语言模型难以反映社会的多样性现实，尤其是边缘化群体细微的生存体验，这也凸显了对模型呈现方式进行批判性审视的必要性。