Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts
arxiv(2024)
摘要
Automatic coding patient behaviors is essential to support decision making
for psychotherapists during the motivational interviewing (MI), a collaborative
communication intervention approach to address psychiatric issues, such as
alcohol and drug addiction. While the behavior coding task has rapidly adapted
machine learning to predict patient states during the MI sessions, lacking of
domain-specific knowledge and overlooking patient-therapist interactions are
major challenges in developing and deploying those models in real practice. To
encounter those challenges, we introduce the Chain-of-Interaction (CoI)
prompting method aiming to contextualize large language models (LLMs) for
psychiatric decision support by the dyadic interactions. The CoI prompting
approach systematically breaks down the coding task into three key reasoning
steps, extract patient engagement, learn therapist question strategies, and
integrates dyadic interactions between patients and therapists. This approach
enables large language models to leverage the coding scheme, patient state, and
domain knowledge for patient behavioral coding. Experiments on real-world
datasets can prove the effectiveness and flexibility of our prompting method
with multiple state-of-the-art LLMs over existing prompting baselines. We have
conducted extensive ablation analysis and demonstrate the critical role of
dyadic interactions in applying LLMs for psychotherapy behavior understanding.
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