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Reflective listening is a fundamental communication skill in behavioral health counseling. It enables counselors to demonstrate an understanding of and empathy for clients’ experiences and concerns. Training to acquire and refine reflective listening skills is essential for counseling proficiency. Yet, it faces significant barriers, notably the need for specialized and timely feedback to improve counseling skills. In this work, we evaluate and compare several computational models, including transformer-based architectures, for their ability to assess the quality of counselors’ reflective listening skills. We explore a spectrum of neural-based models, ranging from compact, specialized RoBERTa models to advanced large-scale language models such as Flan, Mistral, and GPT-3.5, to score psychotherapy reflections. We introduce a psychotherapy dataset that encompasses three basic levels of reflective listening skills. Through comparative experiments, we show that a finetuned small RoBERTa model with a custom learning objective (Prompt-Aware margIn Ranking (PAIR)) effectively provides constructive feedback to counselors in training. This study also highlights the potential of machine learning in enhancing the training process for motivational interviewing (MI) by offering scalable and effective feedback alternatives for counseling training. 

In this paper, we study the problem of multi-reward reinforcement learning to jointly optimize for multiple text qualities for natural language generation. We focus on the task of counselor reflection generation, where we optimize the generators to simultaneously improve the fluency, coherence, and reflection quality of generated counselor responses. We introduce two novel bandit methods, DynaOpt and C-DynaOpt, which rely on the broad strategy of combining rewards into a single value and optimizing them simultaneously. Specifically, we employ non-contextual and contextual multi-arm bandits to dynamically adjust multiple reward weights during training. Through automatic and manual evaluations, we show that our proposed techniques, DynaOpt and C-DynaOpt, outperform existing naive and bandit baselines, showcasing their potential for enhancing language models.