Recent advances in large language models (LLMs) have significantly improved the response quality of dialogue systems. However, the issue of dialogue breakdowns where systems produce utterances that confuse users, such as those containing misinformation or lacking common sense still persists. Since such breakdowns can negatively affect users’ impressions of dialogue systems, it is essential to appropriately repair the flow of conversation. Nevertheless, no existing method can robustly handle a wide range of breakdown types. To address this issue, this study aims to develop a dialogue breakdown repair generation system that can robustly handle various types of dialogue breakdowns. Specifically, we train an LLM using the Dialogue Breakdown Repair Corpus, which contains repair utterances corresponding to diverse breakdown scenarios. As a result, we construct a model specialized in generating repair utterances for various breakdown types and demonstrate that it achieves higher accuracy than existing models.