Scaling Instruction-Finetuned Language Models

Authors: Chowdhery, A., Narang, S., Devlin, J., et al. Year: 2022 Venue: ArXiv ArXiv: https://arxiv.org/abs/2210.11416

Summary

This paper demonstrates that instruction fine-tuning at scale dramatically improves model generalization. PaLM models fine-tuned with diverse instructions achieve state-of-the-art few-shot performance across diverse tasks.

Key Concepts

• Large-scale instruction tuning (540B parameters)
• Diverse task coverage for generalization
• Few-shot performance improvements
• Task mixture importance for transfer
• Scaling benefits of instruction tuning
• Foundation for Flan models and variants

Impact

Scaling Instruction-Finetuned Language Models proved that instruction tuning benefits improve with scale and data diversity. It established best practices for creating instruction-following models across the industry.

Related Papers

• Training Language Models to Follow Instructions with Human Feedback
• The Flan Collection: Designing Data and Methods for Effective Instruction Tuning

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Citation:

@article{chowdhery2022scaling,
  title={Scaling Instruction-Finetuned Language Models},
  author={Chowdhery, Aakanksha and Narang, Sharan and Devlin, Jacob and others},
  journal={arXiv preprint arXiv:2210.11416},
  year={2022}
}