PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning

• URL: http://arxiv.org/abs/2402.12842v3
• Date: Fri, 27 Sep 2024 06:25:33 GMT
• Title: PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning
• Authors: Gyeongman Kim, Doohyuk Jang, Eunho Yang,
• Abstract summary: We propose PromptKD to enable generative language models to transfer student-friendly knowledge. Experiments on instruction-following datasets show that PromptKD achieves state-of-the-art performance. Further analysis suggests that distilling student-friendly knowledge alleviates exposure bias effectively throughout the entire training process.
• Score: 30.70974942397732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in large language models (LLMs) have raised concerns about inference costs, increasing the need for research into model compression. While knowledge distillation (KD) is a prominent method for this, research on KD for generative language models like LLMs is relatively sparse, and the approach of distilling student-friendly knowledge, which has shown promising performance in KD for classification models, remains unexplored in generative language models. To explore this approach, we propose PromptKD, a simple yet effective method that utilizes prompt tuning - for the first time in KD - to enable generative language models to transfer student-friendly knowledge. Unlike previous works in classification that require fine-tuning the entire teacher model for extracting student-friendly knowledge, PromptKD achieves similar effects by adding a small number of prompt tokens and tuning only the prompt with student guidance. Extensive experiments on instruction-following datasets show that PromptKD achieves state-of-the-art performance while adding only 0.0007% of the teacher's parameters as prompts. Further analysis suggests that distilling student-friendly knowledge alleviates exposure bias effectively throughout the entire training process, leading to performance enhancements.

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