SCOPE uses cohort-level external supervision, confidence-aware pseudo-labels, and a lightweight prototype-conditioned adapter (ProAdapter) to adapt frozen EEG foundation models in label-limited settings, reporting consistent gains across 50 experimental configurations.
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UltraChat supplies 1.5 million high-quality multi-turn dialogues that, when used to fine-tune LLaMA, produce UltraLLaMA, which outperforms prior open-source chat models including Vicuna.
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SCOPE: Structured Prototype-Guided Adaptation for EEG Foundation Models with Limited Labels
SCOPE uses cohort-level external supervision, confidence-aware pseudo-labels, and a lightweight prototype-conditioned adapter (ProAdapter) to adapt frozen EEG foundation models in label-limited settings, reporting consistent gains across 50 experimental configurations.
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Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
UltraChat supplies 1.5 million high-quality multi-turn dialogues that, when used to fine-tune LLaMA, produce UltraLLaMA, which outperforms prior open-source chat models including Vicuna.