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.

翻译：大型语言模型（LLMs）在使用人工编写的指令数据完成特定任务时展现出卓越的泛化能力。然而，此类指令数据在数量、多样性和专业深度方面的局限性，引发了人们对LLMs在获得领域特定指令后执行心理治疗任务时性能表现的担忧。为解决这一问题，我们首先基于AlexanderStreet治疗理论提出了领域特定助手指令，其次采用适应性微调方法与检索增强生成技术来改进预训练LLMs。通过自动评估与人工评估相结合的语言质量定量分析，我们发现基于心理治疗助手指令的预训练LLMs在响应生成方面超越了现有最先进的LLMs基线模型。我们的助手指令方法提供了一种半标注技术路径，既能将预训练LLMs与指令要求对齐，又能为预训练LLMs注入更丰富的心理治疗领域知识。