MLP activations measured as massive activations or first four moments correlate weakly (max |Spearman| = 0.33) with in-context example quality across Llama-3.2-3B, Qwen2.5-3B, and multiple classification/generative tasks, so activation-based active learning should not be used for ICL.
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Proposes a multi-modal multi-span medical QA framework and new dataset that outputs answers containing both text and relevant images.
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Activation-Based Active Learning for In-Context Learning: Challenges and Insights
MLP activations measured as massive activations or first four moments correlate weakly (max |Spearman| = 0.33) with in-context example quality across Llama-3.2-3B, Qwen2.5-3B, and multiple classification/generative tasks, so activation-based active learning should not be used for ICL.
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$M^3 QuestionIng$: Multi-modal Multi-span Medical Question Answering
Proposes a multi-modal multi-span medical QA framework and new dataset that outputs answers containing both text and relevant images.