使用助手对心理治疗聊天机器人进行领域特定改进
Domain-Specific Improvement on Psychotherapy Chatbot Using Assistant
AI总结：
本文针对心理治疗领域指令数据不足的问题，提出基于AlexanderStreet疗法的领域特定助手指令，并结合适应性微调与检索增强生成改进预训练LLMs，实验表明其响应质量优于现有基线。
摘要：
大型语言模型（LLMs）在使用人类编写的指令数据时，已在特定任务上展现出令人印象深刻的泛化能力。然而，此类指令数据在数量、多样性和专业水平上的局限性，引发了人们对LLMs在获得领域特定指令时于心理治疗任务中表现的担忧。为解决这一问题，我们首先基于AlexanderStreet疗法提出了领域特定助手指令（Domain-Specific Assistant Instructions），其次采用适应性微调方法和检索增强生成方法来改进预训练LLMs。通过使用自动评估和人工评估对语言质量进行定量评价，我们观察到基于心理治疗助手指令的预训练LLMs优于最先进的LLMs响应基线。我们的助手指令方法提供了一种半标注方法，以使预训练LLMs与指令对齐，并为预训练LLMs提供更多心理治疗知识。
英文摘要：
Large language models (LLMs) have demonstrated impressive generalization capabilities on specific tasks with human-written instruction data. However, the limited quantity, diversity, and professional expertise of such instruction data raise concerns about the performance of LLMs in psychotherapy tasks when provided with domain-specific instructions. To address this, we firstly propose Domain-Specific Assistant Instructions based on AlexanderStreet therapy, and secondly, we use an adaption fine-tuning method and retrieval augmented generation method to improve pre-trained LLMs. Through quantitative evaluation of linguistic quality using automatic and human evaluation, we observe that pre-trained LLMs on Psychotherapy Assistant Instructions outperform state-of-the-art LLMs response baselines. Our Assistant-Instruction approach offers a half-annotation method to align pre-trained LLMs with instructions and provide pre-trained LLMs with more psychotherapy knowledge.
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。
