KARITA integrates knowledge-driven augmentation and retrieval to improve classification performance under temporal shifts across clinical, legal, and scientific domains.
Modeling Temporality of Human Intentions by Domain Adaptation
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Multimodal self-consistency with audio-language models reaches 52.56% accuracy on utterance-level MI coding from five audio sessions, beating single-pass baselines.
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Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation
KARITA integrates knowledge-driven augmentation and retrieval to improve classification performance under temporal shifts across clinical, legal, and scientific domains.
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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.