Selecting Large Language Model to Fine-tune via Rectified Scaling Law
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866910460793585664 |
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| author | Lin, Haowei Huang, Baizhou Ye, Haotian Chen, Qinyu Wang, Zihao Li, Sujian Ma, Jianzhu Wan, Xiaojun Zou, James Liang, Yitao |
| author_facet | Lin, Haowei Huang, Baizhou Ye, Haotian Chen, Qinyu Wang, Zihao Li, Sujian Ma, Jianzhu Wan, Xiaojun Zou, James Liang, Yitao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02314 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Selecting Large Language Model to Fine-tune via Rectified Scaling Law Lin, Haowei Huang, Baizhou Ye, Haotian Chen, Qinyu Wang, Zihao Li, Sujian Ma, Jianzhu Wan, Xiaojun Zou, James Liang, Yitao Machine Learning Artificial Intelligence Computation and Language 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. |
| title | Selecting Large Language Model to Fine-tune via Rectified Scaling Law |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2402.02314 |