@inproceedings{hoehn-etal-2025-speakers,
    title = "On Speakers' Identities, Autism Self-Disclosures and {LLM}-Powered Robots",
    author = "Hoehn, Sviatlana  and
      Philippy, Fred  and
      Andre, Elisabeth",
    editor = "B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Lef{\`e}vre, Fabrice  and
      Asher, Nicholas  and
      Kim, Seokhwan  and
      Merlin, Teva",
    booktitle = "Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = aug,
    year = "2025",
    address = "Avignon, France",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-10/2025.sigdial-1.40/",
    pages = "484--503",
    abstract = "Dialogue agents become more engaging through recipient design, which needs user-specific information. However, a user{'}s identification with marginalized communities, such as migration or disability background, can elicit biased language. This study compares LLM responses to neurodivergent user personas with disclosed vs. masked neurodivergent identities. A dataset built from public Instagram comments was used to evaluate four open-source models on story generation, dialogue generation, and retrieval-augmented question answering. Our analyses show biases in user{'}s identity construction across all models and tasks. Binary classifiers trained on each model can distinguish between language generated for prompts with or without self-disclosures, with stronger biases linked to more explicit disclosures. Some models' safety mechanisms result in denial of service behaviors. LLM{'}s recipient design to neurodivergent identities relies on stereotypes tied to neurodivergence."
}