@inproceedings{zhang-2025-auradial,
    title = "{A}ura{D}ial: A Large-Scale Human-Centric Dialogue Dataset for {C}hinese {AI} Psychological Counseling",
    author = "Zhang, Xiantao",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.155/",
    doi = "10.18653/v1/2025.findings-emnlp.155",
    pages = "2847--2863",
    ISBN = "979-8-89176-335-7",
    abstract = "This paper introduces AuraDial, a large-scale, human-centric dialogue dataset for Chinese AI psychological counseling, comprising over 300,000 single-turn dialogues and 90,000 multi-turn dialogue sessions. A key distinction of AuraDial is its instruction set, primarily derived from real-world user queries, better reflecting genuine expression patterns compared to synthetic or template-based alternatives. Furthermore, we propose an innovative rephrasing-based data generation methodology designed to foster more human-like and empathetic responses, addressing a common shortcoming in AI-generated dialogue. Experimental results demonstrate that models fine-tuned on AuraDial significantly outperform those trained on other public datasets in generating empathetic and relevant replies. AuraDial offers a novel, valuable resource to the Chinese NLP community for advancing AI in psychological counseling. The dataset is publicly available at [https://huggingface.co/datasets/Mxode/AuraDial](https://huggingface.co/datasets/Mxode/AuraDial)."
}