New framework MOCC-R1 improves multimodal counselor response consistency

Researchers have introduced MOCC-R1, a novel framework designed to enhance the consistency between reasoning and response generation in multimodal counselor systems. This framework addresses limitations in existing datasets by utilizing MOCC, a new corpus of over 200 hours of counseling interactions from 154 verified counselors. MOCC-R1 employs a two-stage approach, beginning with supervised fine-tuning to generate structured trajectories including client-state understanding and response intent, followed by reinforcement learning to optimize plan coherence and execution. AI

IMPACT This research could lead to more reliable and trustworthy AI systems for mental health support by improving consistency in counselor response generation.

RANK_REASON The cluster describes a new research paper detailing a novel framework and dataset for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• MOCC-R1
• Multimodal Counselor Response Generation
• reinforcement learning
• ScienceCast
• supervised fine-tuning

• paper
• model release

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