Papers with data-free distillation method
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models (2024.findings-acl)
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Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, Vikas Chandra
| Challenge: | Several post-training quantization methods have been shown to perform well down to 8-bits. |
| Approach: | They propose a data-free distillation method that leverages generations produced by the pre-trained model to quantize any generative model independent of its training data. |
| Outcome: | The proposed method outperforms SoTA PTQ and LLaMA models at low bit precision. |
Adversarial Self-Supervised Data-Free Distillation for Text Classification (2020.emnlp-main)
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| Challenge: | Existing knowledge distillation algorithms rely on the accessibility of the training dataset, which may be unavailable due to privacy issues. |
| Approach: | They propose a data-free distillation method for a pre-trained transformer-based model that uses plug & play Embedding Guessing to craft pseudo embeddings from the teacher's hidden knowledge. |
| Outcome: | The proposed method is the first data-free distillation framework designed for NLP tasks. |