Knowledge Distillation for Closed-Source Language Models

Hongzhan Chen, Xiaojun Quan, Hehong Chen, Ming Yan, Ji Zhang

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Keywords: Knowledge Distillation, Language Model

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Abstract:

Closed-source language models such as GPT-4 have achieved remarkable performance. Recently, many studies have focused on enhancing the capabilities of smaller models,
through knowledge distillation (KD) on those closed-source language models.
However, due to the inability to directly access the closed-source language model's output distribution, KD methods can currently only be performed using one-hot labels, which hinders the effectiveness of KD.
To address this limitation,
we propose a Bayesian estimation-based knowledge distillation method. Specifically, our method comprises prior estimation and posterior estimation. The prior estimation obtains a prior distribution by leveraging the corpus generated by the closed-source language model. The posterior estimation updates the prior distribution to obtain a posterior distribution, based on continued sampling results.
Then we utilize the prior and posterior distributions for distillation.
Experimental results showcase that, in the context of KD for closed-source language model, our method outperforms the current KD methods that directly fine-tune on the one-hot labels.

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Submission Number: 2382