Context-Emotion Aware Therapeutic Dialogue Generation: A Multi-component Reinforcement Learning Approach to Language Models for Mental Health Support

• URL: http://arxiv.org/abs/2511.11884v1
• Date: Fri, 14 Nov 2025 21:32:10 GMT
• Title: Context-Emotion Aware Therapeutic Dialogue Generation: A Multi-component Reinforcement Learning Approach to Language Models for Mental Health Support
• Authors: Eric Hua Qing Zhang, Julia Ive,
• Abstract summary: Mental health illness represents a substantial global socioeconomic burden.<n>This paper investigated the application of supervised fine-tuning and reinforcement learning techniques to enhance GPT-2's capacity for therapeutic dialogue generation.
• Score: 3.857814030650221
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mental health illness represents a substantial global socioeconomic burden, with COVID-19 further exacerbating accessibility challenges and driving increased demand for telehealth mental health support. While large language models (LLMs) offer promising solutions through 24/7 availability and non-judgmental interactions, pre-trained models often lack the contextual and emotional awareness necessary for appropriate therapeutic responses. This paper investigated the application of supervised fine-tuning (SFT) and reinforcement learning (RL) techniques to enhance GPT-2's capacity for therapeutic dialogue generation. The methodology restructured input formats to enable simultaneous processing of contextual information and emotional states alongside user input, employing a multi-component reward function that aligned model outputs with professional therapist responses and annotated emotions. Results demonstrated improvements through reinforcement learning over baseline GPT-2 across multiple evaluation metrics: BLEU (0.0111), ROUGE-1 (0.1397), ROUGE-2 (0.0213), ROUGE-L (0.1317), and METEOR (0.0581). LLM evaluation confirmed high contextual relevance and professionalism, while reinforcement learning achieved 99.34% emotion accuracy compared to 66.96% for baseline GPT-2. These findings demonstrate reinforcement learning's effectiveness in developing therapeutic dialogue systems that can serve as valuable assistive tools for therapists while maintaining essential human clinical oversight.

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