Multimodal self-consistency with audio-language models reaches 52.56% accuracy on utterance-level MI coding from five audio sessions, beating single-pass baselines.
A scoping review of large language models for generative tasks in mental health care
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Leveraging Multimodal Self-Consistency Reasoning in Coding Motivational Interviewing for Alcohol Use Reduction
Multimodal self-consistency with audio-language models reaches 52.56% accuracy on utterance-level MI coding from five audio sessions, beating single-pass baselines.