Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.15437 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917216767705088 |
|---|---|
| author | Park, Sohyeon Beltran, Jesus Armando Min, Aehong Ritt-Olson, Anamara Hayes, Gillian R. |
| author_facet | Park, Sohyeon Beltran, Jesus Armando Min, Aehong Ritt-Olson, Anamara Hayes, Gillian R. |
| contents | Large Language Models (LLMs) like ChatGPT offer potential support for autistic people, but this potential requires understanding the implicit perspectives these models might carry, including their biases and assumptions about autism. Moving beyond single-agent prompting, we utilized LLM-based multi-agent systems to investigate complex social scenarios involving autistic and non-autistic agents. In our study, agents engaged in group-task conversations and answered structured interview questions, which we analyzed to examine ChatGPT's biases and how it conceptualizes autism. We found that ChatGPT assumes autistic people are socially dependent, which may affect how it interacts with autistic users or conveys information about autism. To address these challenges, we propose adopting the double empathy problem, which reframes communication breakdowns as a mutual challenge. We describe how future LLMs could address the biases we observed and improve interactions involving autistic people by incorporating the double empathy problem into their design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15437 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Exploring Implicit Perspectives on Autism in Large Language Models Through Multi-Agent Simulations Park, Sohyeon Beltran, Jesus Armando Min, Aehong Ritt-Olson, Anamara Hayes, Gillian R. Human-Computer Interaction Large Language Models (LLMs) like ChatGPT offer potential support for autistic people, but this potential requires understanding the implicit perspectives these models might carry, including their biases and assumptions about autism. Moving beyond single-agent prompting, we utilized LLM-based multi-agent systems to investigate complex social scenarios involving autistic and non-autistic agents. In our study, agents engaged in group-task conversations and answered structured interview questions, which we analyzed to examine ChatGPT's biases and how it conceptualizes autism. We found that ChatGPT assumes autistic people are socially dependent, which may affect how it interacts with autistic users or conveys information about autism. To address these challenges, we propose adopting the double empathy problem, which reframes communication breakdowns as a mutual challenge. We describe how future LLMs could address the biases we observed and improve interactions involving autistic people by incorporating the double empathy problem into their design. |
| title | Exploring Implicit Perspectives on Autism in Large Language Models Through Multi-Agent Simulations |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.15437 |