Shiny Stories, Hidden Struggles: Investigating the Representation of Disability Through the Lens of LLMs
Quick Take
LLMs often misrepresent disability by idealizing experiences and reinforcing biases against marginalized groups.
Key Points
- LLMs create overly positive stereotypes of disabilities.
- Comparative analysis shows bias favoring nondisabled individuals.
- Critical scrutiny of LLM representations is essential.
📖 Reader Mode
~3 min readAbstract: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.
| Comments: | Accepted for publication in ACM Transactions on Intelligent Systems and Technology |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.20191 [cs.CL] |
| (or arXiv:2605.20191v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20191 arXiv-issued DOI via DataCite |
|
| Journal reference: | ACM Trans. Intell. Syst. Technol. (2026) |
| Related DOI: | https://doi.org/10.1145/3806202
DOI(s) linking to related resources |
Submission history
From: Marco Bombieri [view email]
[v1]
Thu, 2 Apr 2026 08:39:42 UTC (2,051 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →arXiv cs.CL·Leyao Wang, Yanan He, Peng Chen, Asaf Yehudai, Yixin Liu, Rex Ying, Michal Shmueli-Scheuer, Arman Cohan
1d ago FeaturedOriginal
Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
AI Summary
The reliability of LLM judges for evaluating deep research agents is critically assessed using the REFLECT benchmark.
