@inproceedings{lachenmaier-etal-2026-talking,
    title = "Talking to a Know-It-All {GPT} or a Second-Guesser Claude? How Repair reveals distinct Multi-Turn Behavior in {LLM}s",
    author = "Lachenmaier, Clara  and
      Bultmann, Hannah  and
      Zarrie{\{}{\textbackslash}ss{\}}, Sina",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.651/",
    pages = "14312--14325",
    ISBN = "979-8-89176-390-6",
    abstract = "Repair, an important resource for resolving trouble in human{--}human conversation, remains underexplored in human{--}LLM interaction.In this study, we investigate how LLMs engage in the interactive process of repair in multi-turn dialogues around solvable and unsolvable math questions. We examine whether models initiate repair themselves and how they respond to user-initiated repair. Our results show strong differences across models: reactions range from being almost completely resistant to (appropriate) repair attempts to being highly susceptible and easily manipulated. We further demonstrate that once conversations extend beyond a single turn, model behavior becomes more distinctive and less predictable across systems. Overall, our findings indicate that each tested LLM exhibits its own characteristic form of unreliability in the context of repair."
}