KARITA integrates knowledge-driven augmentation and retrieval to improve classification performance under temporal shifts across clinical, legal, and scientific domains.
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HAMR combines meta-learning with hardness-aware weighting and neighborhood resampling to improve minority-class performance on imbalanced NLP datasets.
Multimodal self-consistency with audio-language models reaches 52.56% accuracy on utterance-level MI coding from five audio sessions, beating single-pass baselines.
citing papers explorer
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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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Model-Agnostic Meta Learning for Class Imbalance Adaptation
HAMR combines meta-learning with hardness-aware weighting and neighborhood resampling to improve minority-class performance on imbalanced NLP datasets.
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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.