  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.
