CODI compresses explicit CoT into continuous space via self-distillation and is the first implicit method to match explicit CoT performance on GSM8k at GPT-2 scale with 3.1x compression and 28.2% higher accuracy than prior implicit approaches.
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AgriKD distills multi-level knowledge from Vision Transformers to lightweight CNNs, achieving comparable leaf disease classification accuracy with 172x fewer parameters and 18-22x faster inference.
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CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation
CODI compresses explicit CoT into continuous space via self-distillation and is the first implicit method to match explicit CoT performance on GSM8k at GPT-2 scale with 3.1x compression and 28.2% higher accuracy than prior implicit approaches.
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AgriKD: Cross-Architecture Knowledge Distillation for Efficient Leaf Disease Classification
AgriKD distills multi-level knowledge from Vision Transformers to lightweight CNNs, achieving comparable leaf disease classification accuracy with 172x fewer parameters and 18-22x faster inference.