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arXiv · 2401.07013

Knowledge Distillation of Black-Box Large Language Models

Hongzhan Chen·Ruijun Chen·Yuqi Yi·Xiaojun Quan·Chenliang Li·Ming Yan·Ji Zhang
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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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Hongzhan Chen, Ruijun Chen, Yuqi Yi, Xiaojun Quan, Chenliang Li, Ming Yan, Ji Zhang. 2024-01-13. Knowledge Distillation of Black-Box Large Language Models. https://arxiv.org/abs/2401.07013

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