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2307.14430.pdf
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🔗 Source: arxiv.org
📊 Size: 1.92 MB
📄 Pages: 37 pages
⬇️ Downloads: 174

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Researchers propose a data-driven skills framework, Skill-it, to improve the training of large language models (LMs) by selecting the most effective data given a fixed budget of tokens. The framework is based on the idea that LMs learn skills in a particular order, similar to how humans acquire knowledge. The researchers define a skill as a unit of behavior that a model can learn using an associated slice of data and an ordered skill set as a collection of skills with a directed skills graph. They demonstrate that ordered skill sets exist in synthetic and real datasets and that training on a mixture of a skill and its prerequisite skills can lead to faster learning. The researchers propose two methods for selecting data: skill-stratified sampling and an online generalization, SKILL-IT, which addresses the issue of unevenly distributed skills in datasets. SKILL-IT is shown to outperform random sampling in both continual pre-training and fine-tuning settings, achieving higher accuracy and lower validation loss.

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Researchers propose a data-driven skills framework, Skill-it, to improve the training of large language models (LMs) by selecting the most effective data given...

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  • File Format: PDF
  • File Size: 1.92 MB
  • Pages: 37
  • Language: EN
  • Total Downloads: 174
  • Last Updated: 1 week ago

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