Summary of Get More For Less: Principled Data Selection For Warming Up Fine-tuning in Llms, by Feiyang Kang et al.
Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs
by Feiyang Kang, Hoang Anh Just, Yifan Sun, Himanshu Jahagirdar, Yuanzhi Zhang, Rongxing Du, Anit Kumar Sahu, Ruoxi Jia
First submitted to arxiv on: 5 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This paper proposes a novel approach to pre-fine-tune large language models using vast amounts of unlabeled data. The goal is to minimize the need for costly domain-specific data while achieving desired performance levels. Unlike existing methods that prioritize data aligning with the target distribution, this work selects data that nudges the pre-training distribution closer to the target distribution. The authors demonstrate the optimality of this approach under certain conditions and show its efficacy across various natural language understanding (NLU) and generation (NLG) tasks using models up to 2.7B parameters. Their method is also significantly faster than existing techniques, scaling to millions of samples within a single GPU hour. This work aims to lay the groundwork for cost-effective fine-tuning, making its benefits more accessible. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better use big language models by finding the right data to make them work even better. Right now, it’s expensive and time-consuming to train these models on specific tasks. The authors came up with a new way to prepare the models for these tasks using lots of free online data. This method is faster and more effective than what we have now. It works well across many different language tasks and could make big language models more accessible and useful. |
Keywords
» Artificial intelligence » Fine tuning » Language understanding
