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home / papers / 2023 / The Flan Collection: Designing Data and Methods for Effective Instruction Tuning

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

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2301.13688

Summary

Instruction tuning trains language models on many NLP tasks rephrased as instructions. Several public collections already existed, but it was unclear which design choices actually drive the gains. This paper analyzes the recipe behind the Flan 2022 Collection, first used by Chung et al. (2022). The collection merges Flan 2021, P3++, Super-Natural Instructions and additional reasoning, dialog and program-synthesis data, and adds hundreds of new templates. The authors keep the base model fixed (T5-LM, XL size, 3B) and run ablations on four factors:

  • mixing zero-shot, few-shot and chain-of-thought formats during training
  • scaling to more than 1800 tasks
  • input inversion
  • balancing task sources

The resulting Flan-T5 outperforms equally sized models tuned on earlier public collections by about 3–17%. It also beats the much larger OPT-IML-Max 175B. In single-task finetuning, Flan-T5 converges faster and reaches higher scores than plain T5, which makes it a cheaper starting point for new tasks. The data, templates and generation code are released publicly, giving open research a shared, strong baseline for instruction tuning.

Key points

  • Collection and release: Flan 2022 covers 1800+ tasks. The generation code is available at github.com/google-research/FLAN (flan/v2) and lets users adjust mixture rates, templates, prompt types and augmentations.
  • Mixed prompt formats: Training on a blend of zero-shot, few-shot and CoT formats improves every setting at once. Adding only about 10% few-shot examples raises zero-shot accuracy by more than 2%.
  • Benchmark gains: Compared with the next best T5-XL model, the gains are 4.2%+ on MMLU and 8.5% on BIG-Bench Hard (BBH). The comparison covers Flan 2021, P3++/T0++, Super-Natural Instructions and OPT-IML.
  • Underrated techniques: Input inversion (swapping inputs and outputs to create new tasks) and balancing the mixture of task sources both proved critical.
  • Evaluation setup: The authors used 8 Held-In tasks (4 QA and 4 NLI), 5 CoT validation sets, and Held-Out MMLU and BBH. All models are 3B T5-XL, except the OPT-IML-Max 175B comparison.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

479 papers of our library cite it (of the 140673 whose bibliographies we read); 47 of them have a page here, 432 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2301.13688 · arXiv 2301.13688

@misc{longpre2023flan,
  title = {{The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}},
  author = {Longpre, Shayne and Hou, Le and Vu, Tu and Webson, Albert and Chung, Hyung Won and Tay, Yi and Zhou, Denny and Le, Quoc V. and Zoph, Barret and Wei, Jason and Roberts, Adam},
  year = {2023},
  doi = {10.48550/arxiv.2301.13688},
  eprint = {2301.13688},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2301.13688}
}

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