Domain-Specific Improvement on Psychotherapy Chatbot Using Assistant
<|reference_start|>Domain-Specific Improvement on Psychotherapy Chatbot Using Assistant: 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.<|reference_end|>
@article{kang2024domain-specific,
    title={Domain-Specific Improvement on Psychotherapy Chatbot Using Assistant},
    author={Cheng Kang and Daniel Novak and Katerina Urbanova and Yuqing Cheng and
  Yong Hu},
    journal={arXiv preprint arXiv:2404.16160},
    year={2024},
    doi={10.1109/ICASSPW62465.2024.10626529},
    archivePrefix={arXiv},
    eprint={2404.16160},
    primaryClass={cs.CL cs.AI}
}