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Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
March 1, 2024, 5:44 a.m. | Kai Huang, Hanyun Yin, Heng Huang, Wei Gao
cs.LG updates on arXiv.org arxiv.org
Abstract: Fine-tuning is the most effective way of adapting pre-trained large language models (LLMs) to downstream applications. With the fast growth of LLM-enabled AI applications and democratization of open-souced LLMs, fine-tuning has become possible for non-expert individuals, but intensively performed LLM fine-tuning worldwide could result in significantly high energy consumption and carbon footprint, which may bring large environmental impact. Mitigating such environmental impact towards Green AI directly correlates to reducing the FLOPs of fine-tuning, but existing …
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