A ura D ial: A Large-Scale Human-Centric Dialogue Dataset for C hinese AI Psychological Counseling (2025)

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).

## introduction mental health is a growing global public health concern @xcite . artificial intelligence shows significant potential in providing accessible, scalable psychological support @xcite , and integrating such support into conversational ai has profound social significance @xcite @xcite . developing culturally aware ai counseling resources is crucial, particularly for the chinese context, where high-quality datasets are foundational but scarce @xcite @xcite . to address this scarcity, the field has increasingly relied on synthetic data generation using large language models (llms) @xcite @xcite . while this approach enables the creation of large-scale datasets, it has introduced a distinct set of challenges that limit the development of genuinely effective ai counselors. a primary issue lies in the authenticity and diversity of the generated content. instructions are often synthetic or template-based, failing to capture the complexity, emotional nuance, and ambiguity characteristic of real-world help-seeking expressions @xcite . consequently, ai-generated responses often lack deep empathy, exhibiting an impersonal quality commonly described as an "ai feel" that can alienate users and erode trust @xcite . this reliance on purely synthetic dialogues risks creating an echo chamber, where models learn the stylistic biases of their predecessors rather than the patterns of genuine human interaction, leading to limited thematic coverage and poor generalization to real-world scenarios @xcite . our contributions to address these gaps, we present auradial, a large-scale, human-centric dialogue dataset for chinese psychological counseling, with over 300,000 single-turn and over 90,000 multi-turn dialogues. our key contributions are: ## experiments we conducted comparative experiments to validate auradial's effectiveness, particularly for enhancing model empathy. ## conclusion this paper introduced auradial, a large-scale, human-centric dataset for chinese ai psychological counseling, designed to overcome key limitations in existing resources: inauthentic instructions and synthetic responses that lack empathy. our work makes two primary contributions: we ground our dataset in a large and diverse set of real-world user queries, and we propose a novel rephrasingbased synthesis pipeline to generate counselor responses. this methodology deliberately constrains the generation task to focus on enhancing stylistic and emotional nuance, proving more effective for cultivating empathetic style than direct generation. our experimental results robustly validate this approach. in blind, pairwise evaluations, a model fine-tuned on auradial significantly outperformed models trained on four other public datasets, demonstrating its superior ability to generate empathetic and relevant replies. by providing both a high-quality, large-scale dataset and a principled methodology for its creation, we believe au-radial will serve as a foundational resource for the community. it paves the way for the development of more nuanced, trustworthy, and genuinely supportive ai counseling systems, ultimately contributing to more human-centric conversational ai.