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黑盒大语言模型的知识蒸馏
阅读原文· arxiv.orgAI 摘要
GPT-4 等闭源大语言模型性能优异，但因其作为黑盒教师无法提供内部状态，限制了知识蒸馏的效果。Proxy-KD 方法引入一个代理模型，实现从黑盒 LLM 到小模型的高效知识迁移。实验结果显示，Proxy-KD 不仅提升了黑盒教师蒸馏的性能，还超越了传统白盒蒸馏技术。
原文 · 保持原样，未翻译
Computer Science > Computation and Language
[Submitted on 13 Jan 2024 (v1), last revised 9 Nov 2024 (this version, v2)]
Title:Knowledge Distillation of Black-Box Large Language Models
View PDF HTML (experimental)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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2401.07013 [cs.CL] |
| (or arXiv:2401.07013v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2401.07013 arXiv-issued DOI via DataCite |
Submission history
From: Hongzhan Chen [view email][v1] Sat, 13 Jan 2024 08:43:32 UTC (359 KB)
[v2] Sat, 9 Nov 2024 01:35:32 UTC (8,288 KB)
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