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.
与全知GPT或犹豫Claude对话：修复机制如何揭示大语言模型在多轮对话中的不可靠行为 / Talking to a Know-It-All GPT or a Second-Guesser Claude? How Repair reveals unreliable Multi-Turn Behavior in LLMs
本文通过让多种大语言模型（如GPT和Claude）在数学问答的多轮对话中应对正确或错误的问题，发现不同模型在察觉自身错误、接受用户纠正或坚持错误方面表现出截然不同的行为模式，且对话轮次越多，模型的行为越不可预测，各有其独特的不可靠性特征。
源自 arXiv: 2604.19245
