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：大规模语言模型（LLMs）在基于人类撰写的指令数据的特定任务上展现了令人印象深刻的泛化能力。然而，这类指令数据在数量、多样性和专业领域知识方面的局限性，引发了人们对LLMs在心理治疗任务中，当提供领域特定指令时性能表现的担忧。为解决此问题，我们首先基于亚历山大疗法提出了领域特定的助手指令，其次，我们采用自适应微调方法和检索增强生成方法来改进预训练的LLMs。通过自动评估和人工评估对语言质量进行量化评价，我们观察到基于心理治疗助手指令的预训练LLMs在响应基线方面优于最先进的LLMs。我们的助手指令方法提供了一种半标注方法，用于将预训练的LLMs与指令对齐，并为预训练的LLMs提供更多的心理治疗知识。