Efficient and responsible transformer based conversational agents for emotionally supportive dialogue

Abstract Conversational agents designed for emotionally supportive interactions face challenges in balancing affective responsiveness, computational efficiency, and safety in communication. Prior approaches frequently depend on large-scale models, handcrafted affective objectives, or reinforcement learning from human feedback, which can limit scalability and interpretability. This work presents a lightweight, domain-adapted dialogue generation system based on the T5-small architecture, fine-tuned on MentalChat16K, a curated corpus of real and synthetic emotional-support conversations. The proposed model operates without reinforcement learning or emotion-specific training objectives, yet demonstrates encouraging alignment with affective cues and fluent response generation within the evaluated dataset. Empirical evaluation shows improvements over zero-shot and fine-tuned GPT-2 baselines, achieving BLEU (32.14), ROUGE-L (44.72), and BERTScore-F1 (85.11). Expert human assessments indicated high ratings in coherence, emotional appropriateness, and contextual relevance, with substantial inter-rater agreement. Qualitative error analysis indicated generally conservative and context-aware responses within the evaluated sample. During manual review of this sample, no factual hallucinations, medical overreach, or overtly unsafe responses were observed; however, systematic safety benchmarking was beyond the scope of the present study. This study provides initial evidence that compact transformer-based models, when adapted to domain-specific corpora and evaluated under controlled conditions, can support efficient and affectively appropriate dialogue generation in emotionally supportive non-clinical settings, while requiring further safety validation before broader real-world deployment.