Domain-Specific Improvement on Psychotherapy Chatbot Using Assistant
Cheng Kang, Daniel Novak, Katerina Urbanova, Yuqing Cheng and, Yong Hu

TL;DR
This paper introduces domain-specific instructions and adaptation techniques to enhance large language models' performance in psychotherapy tasks, demonstrating significant improvements over existing models through evaluation.
Contribution
It proposes psychotherapy-specific assistant instructions and adapts fine-tuning and retrieval methods to improve LLMs in psychotherapy applications.
Findings
Pre-trained LLMs with psychotherapy instructions outperform state-of-the-art baselines.
The approach improves linguistic quality of responses in psychotherapy tasks.
The method effectively aligns LLMs with domain-specific knowledge.
Abstract
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…
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Taxonomy
TopicsMental Health via Writing · Digital Mental Health Interventions · Artificial Intelligence in Healthcare and Education
MethodsALIGN
