Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Prompting
<|reference_start|>Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Prompting: Existing dialogue data augmentation (DA) techniques predominantly focus on augmenting utterance-level dialogues, which makes it difficult to take dialogue contextual information into account. The advent of large language models (LLMs) has simplified the implementation of multi-turn dialogues. Due to absence of professional understanding and knowledge, it remains challenging to deliver satisfactory performance in low-resource domain, like psychological dialogue dialogue. DA involves creating new training or prompting data based on the existing data, which help the model better understand and generate psychology-related responses. In this paper, we aim to address the issue of multi-turn dialogue data augmentation for boosted performance in the psychology domain. We propose a knowledge-driven progressive thought prompting method to guide LLM to generate multi-turn psychology-related dialogue. This method integrates a progressive thought generator, a psychology knowledge generator, and a multi-turn dialogue generator. The thought generated by the progressive thought generator serves as a prompt to prevent the generated dialogue from having significant semantic deviations, while the psychology knowledge generator produces psychological knowledge to serve as the dialogue history for the LLM, guiding the dialogue generator to create multi-turn psychological dialogue. To ensure the precision of multi-turn psychological dialogue generation by LLM, a meticulous professional evaluation is required. Extensive experiments conducted on three datasets related to psychological dialogue verify the effectiveness of the proposed method.<|reference_end|>
@article{jiang2024data,
    title={Data Augmentation of Multi-turn Psychological Dialogue via
  Knowledge-driven Progressive Thought Prompting},
    author={Jiyue Jiang, Liheng Chen, Sheng Wang, Lingpeng Kong, Yu Li, and Chuan
  Wu},
    journal={arXiv preprint arXiv:2406.16567},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16567},
    primaryClass={cs.CL}
}