Summary of An Empirical Study Of Scaling Laws For Transfer, by Matthew Barnett
An Empirical Study of Scaling Laws for Transfer
by Matthew Barnett
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel empirical study investigates scaling laws for transfer learning in transformer models. The research explores a scaling law that incorporates a “transfer gap” term, measuring the effectiveness of pre-training on one distribution when optimizing for downstream performance on another distribution. When the transfer gap is low, pre-training proves to be an effective strategy for improving downstream performance. Conversely, when the gap is high, collecting high-quality fine-tuning data becomes relatively more cost-effective. The study finds significant variations in the transfer gap across distributions, with potential implications for optimal data allocation strategies and understanding how data scarcity affects capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study looks at how to make machine learning models better by using information learned from one task on another task. The researchers found a way to measure how well this “transfer learning” works, depending on the difference between the two tasks. When the difference is small, it’s good to use old knowledge to help with the new task. But when the difference is big, it’s better to collect more data for the new task. The study shows that different tasks have different levels of difficulty in using old knowledge, which can help us make decisions about how to prepare for new tasks. |
Keywords
» Artificial intelligence » Fine tuning » Machine learning » Scaling laws » Transfer learning » Transformer
