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.

Communication among humans relies on conversational grounding, allowing interlocutors to reach mutual understanding even when they do not have perfect knowledge and must resolve discrepancies in each other’s beliefs. This paper investigates how large language models (LLMs) manage common ground in cases where they (don’t) possess knowledge, focusing on facts in the political domain where the risk of misinformation and grounding failure is high. We examine LLMs’ ability to answer direct knowledge questions and loaded questions that presuppose misinformation.We evaluate whether loaded questions lead LLMs to engage in active grounding and correct false user beliefs, in connection to their level of knowledge and their political bias.Our findings highlight significant challenges in LLMs’ ability to engage in grounding and reject false user beliefs, raising concerns about their role in mitigating misinformation in political discourse.