SCOUT combines progressive distillation with a cross-attention module to make recursive latent reasoning work through fine-tuning, yielding up to 1.8% accuracy gains over standard fine-tuning.
Knowledge Distillation of Black-Box Large Language Models
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abstract
Given the exceptional performance of proprietary large language models (LLMs) like GPT-4, recent research has increasingly focused on boosting the capabilities of smaller models through knowledge distillation (KD) from these powerful yet black-box teachers. While leveraging the high-quality outputs of these teachers is advantageous, the inaccessibility of their internal states often limits effective knowledge transfer. To overcome this limitation, we introduce Proxy-KD, a novel method that uses a proxy model to facilitate the efficient transfer of knowledge from black-box LLMs to smaller models. Our experiments show that Proxy-KD not only enhances the performance of KD from black-box teacher models but also surpasses traditional white-box KD techniques.~This approach presents a compelling new avenue for distilling knowledge from advanced LLMs.
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SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought
SCOUT combines progressive distillation with a cross-attention module to make recursive latent reasoning work through fine-tuning, yielding up to 1.8% accuracy gains over standard fine-tuning.