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

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 ( L LMs) 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 - tu ning (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 me trics: 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 therap eutic dialogue systems that can serve as valuable assistive tools for therapists while maintaining essential human clinical oversight. The code and a ppendic es are publicly available at: https://github.com/ez