EmoStage improves LLM counseling responses by prompting models to first take the client's perspective and recognize the counseling stage, with no training data.
Can Large Language Models be Used to Provide Psychological Counselling? An Analysis of GPT-4-Generated Responses Using Role-play Dialogues
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abstract
Mental health care poses an increasingly serious challenge to modern societies. In this context, there has been a surge in research that utilizes information technologies to address mental health problems, including those aiming to develop counseling dialogue systems. However, there is a need for more evaluations of the performance of counseling dialogue systems that use large language models. For this study, we collected counseling dialogue data via role-playing scenarios involving expert counselors, and the utterances were annotated with the intentions of the counselors. To determine the feasibility of a dialogue system in real-world counseling scenarios, third-party counselors evaluated the appropriateness of responses from human counselors and those generated by GPT-4 in identical contexts in role-play dialogue data. Analysis of the evaluation results showed that the responses generated by GPT-4 were competitive with those of human counselors.
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2025 1verdicts
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EmoStage: A Framework for Accurate Empathetic Response Generation via Perspective-Taking and Phase Recognition
EmoStage improves LLM counseling responses by prompting models to first take the client's perspective and recognize the counseling stage, with no training data.