Selecting Large Language Model to Fine-tune via Rectified Scaling Law

• URL: http://arxiv.org/abs/2402.02314v3
• Date: Tue, 28 May 2024 16:16:42 GMT
• Title: Selecting Large Language Model to Fine-tune via Rectified Scaling Law
• Authors: Haowei Lin, Baizhou Huang, Haotian Ye, Qinyu Chen, Zihao Wang, Sujian Li, Jianzhu Ma, Xiaojun Wan, James Zou, Yitao Liang,
• Abstract summary: Given constrained resources, fine-tuning all models and making selections afterward is unrealistic. We find that the fine-tuning scaling curve includes not just the well-known "power phase" but also the previously unobserved "pre-power phase" By leveraging our law, we propose a novel LLM selection algorithm that selects the near-optimal model with hundreds of times less resource consumption.
• Score: 74.84096546112215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ever-growing ecosystem of LLMs has posed a challenge in selecting the most appropriate pre-trained model to fine-tune amidst a sea of options. Given constrained resources, fine-tuning all models and making selections afterward is unrealistic. In this work, we formulate this resource-constrained selection task into predicting fine-tuning performance and illustrate its natural connection with Scaling Law. Unlike pre-training, we find that the fine-tuning scaling curve includes not just the well-known "power phase" but also the previously unobserved "pre-power phase". We also explain why existing Scaling Law fails to capture this phase transition phenomenon both theoretically and empirically. To address this, we introduce the concept of "pre-learned data size" into our Rectified Scaling Law, which overcomes theoretical limitations and fits experimental results much better. By leveraging our law, we propose a novel LLM selection algorithm that selects the near-optimal model with hundreds of times less resource consumption, while other methods may provide negatively correlated selection. The project page is available at rectified-scaling-law.github.io.

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