[2401.07013] Knowledge Distillation of Black-Box Large Language Models
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Computer Science > Computation and Language
arXiv:2401.07013 (cs)
[Submitted on 13 Jan 2024 (v1), last revised 9 Nov 2024 (this version, v2)]
Title:Knowledge Distillation of Black-Box Large Language Models
Authors:Hongzhan Chen, Ruijun Chen, Yuqi Yi, Xiaojun Quan, Chenliang Li, Ming Yan, Ji Zhang<br>View a PDF of the paper titled Knowledge Distillation of Black-Box Large Language Models, by Hongzhan Chen and 5 other authors
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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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Computation and Language (cs.CL)
Cite as:<br>arXiv:2401.07013 [cs.CL]
(or<br>arXiv:2401.07013v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2401.07013
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arXiv-issued DOI via DataCite
Submission history<br>From: Hongzhan Chen [view email]<br>[v1]<br>Sat, 13 Jan 2024 08:43:32 UTC (359 KB)
[v2]<br>Sat, 9 Nov 2024 01:35:32 UTC (8,288 KB)
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