@inproceedings{chen-etal-2025-consistentchat,
    title = "{C}onsistent{C}hat: Building Skeleton-Guided Consistent Multi-Turn Dialogues for Large Language Models from Scratch",
    author = "Chen, Jiawei  and
      Guan, Xinyan  and
      Yuan, Qianhao  and
      Guozhao, Mo  and
      Zhou, Weixiang  and
      Lu, Yaojie  and
      Lin, Hongyu  and
      He, Ben  and
      Sun, Le  and
      Han, Xianpei",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.424/",
    pages = "8426--8452",
    ISBN = "979-8-89176-332-6",
    abstract = "Current instruction data synthesis methods primarily focus on single-turn instructions and often neglect cross-turn coherence, resulting in context drift and reduced task completion rates in extended conversations. To address this limitation, we propose Skeleton-Guided Multi-Turn Dialogue Generation, a framework that constrains multi-turn instruction synthesis by explicitly modeling human conversational intent. It operates in two stages: (1) Intent Modeling, which captures the global structure of human dialogues by assigning each conversation to one of nine well-defined intent trajectories, ensuring a coherent and goal-oriented information flow; and (2) Skeleton Generation, which constructs a structurally grounded sequence of user queries aligned with the modeled intent, thereby serving as a scaffold that constrains and guides the downstream instruction synthesis process. Based on this process, we construct ConsistentChat, a multi-turn instruction dataset with approximately 15,000 multi-turn conversations and 224,392 utterances. Experiments on the Light, Topdial, and MT-Eval benchmarks show that models fine-tuned on ConsistentChat achieve a 20{--}30{\%} improvement in chat consistency and up to a 15{\%} increase in task success rate, significantly outperforming models trained on existing single-turn and multi-turn instruction datasets."
}