Knowledge Distillation of Black-Box Large Language Models (2024)
arxiv.org
June 28, 2026
2 min read
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47/100
Summary
Knowledge distillation (KD) enhances the performance of smaller models by transferring knowledge from proprietary large language models (LLMs) like GPT-4. This method aims to improve the capabilities of smaller models while utilizing the strengths of black-box teachers.
Key Takeaways
- Proxy-KD is a novel method introduced for knowledge distillation from black-box large language models to smaller models.
- Proxy-KD enhances the performance of knowledge distillation compared to traditional white-box techniques.
- The method addresses the limitations of inaccessibility of internal states in proprietary large language models.
- Research increasingly focuses on improving smaller models by leveraging the high-quality outputs of advanced black-box LLMs.
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