The Flan Collection: Designing Data and Methods for Effective Instruction Tuning

Authors: Longpre, S., Hou, L., Call, A., et al. Year: 2023 Venue: ICLR ArXiv: https://arxiv.org/abs/2301.13688

Summary

The Flan Collection systematically studies instruction tuning by releasing 1.8M task instances across 473 diverse tasks. It demonstrates that mixture composition, task diversity, and instruction diversity significantly impact generalization.

Key Concepts

• 1.8M instruction-output pairs across 473 tasks
• Public benchmark for instruction tuning
• Task mixture and diversity analysis
• Template variations for robustness
• Scaling law insights for instruction tuning
• Open-source data for reproducibility
• Zero-shot and few-shot evaluation framework

Impact

Flan Collection became the standard benchmark for instruction tuning research. It enabled reproducible experiments and established best practices for data collection and task diversity in LLM fine-tuning.

Related Papers

• Scaling Instruction-Finetuned Language Models
• Training Language Models to Follow Instructions with Human Feedback

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Citation:

@inproceedings{longpre2023flan,
  title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
  author={Longpre, Shayne and Hou, Le and Call, Austin and others},
  booktitle={International Conference on Learning Representations},
  year={2023}
}