基于知识驱动的渐进思维提示的多轮心理对话数据增强
Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Prompting
AI总结：
针对现有对话数据增强难兼顾上下文、低资源心理对话领域效果不佳的问题，提出知识驱动的渐进思维提示方法，整合三类生成器引导LLM生成多轮心理对话，经多数据集实验验证有效。
摘要：
现有的对话数据增强（DA）技术主要聚焦于话语级对话的增强，这使得其难以考虑对话的上下文信息。大语言模型（LLMs）的出现简化了多轮对话的实现过程。但由于缺乏专业理解与知识，在心理对话这类低资源领域中，要实现令人满意的性能仍然具有挑战性。数据增强指的是基于现有数据创建新的训练或提示数据，帮助模型更好地理解并生成心理学相关的回复。本文旨在解决多轮对话数据增强问题，以提升心理学领域的相关性能。我们提出了一种知识驱动的渐进思维提示方法，用于引导大语言模型生成多轮心理学相关对话。该方法整合了渐进思维生成器、心理学知识生成器和多轮对话生成器三个模块。渐进思维生成器产生的思维内容作为提示，可防止生成的对话出现明显的语义偏差；而心理学知识生成器会生成心理学知识，作为大语言模型的对话历史，引导对话生成器创建多轮心理对话。为了确保大语言模型生成多轮心理对话的准确性，需要进行细致的专业评估。我们在三个心理对话相关数据集上开展了大量实验，验证了所提方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
