블랙박스 대형 언어 모델의 지식 증류

--> 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 View a PDF of the paper titled Knowledge Distillation of Black-Box Large Language Models, by Hongzhan Chen and 5 other authors 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 Focus to learn more 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) Full-text links: Access Paper: View a PDF of the paper titled Knowledge Distillation of Black-Box Large Language Models, by Hongzhan Chen and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2024-01 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation &times; loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? )