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CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation

Paper · 2025 · Ingo Ziegler, Abdullatif Köksal, Desmond Elliott et al.

Summary

Fine-tuning LLMs for specialized tasks usually works better than prompting, but building large task-specific training sets requires costly manual annotation and domain expertise. Ziegler et al. propose CRAFT (Corpus Retrieval and Augmentation for Fine-Tuning), which starts from only a handful of user-written example tasks. These few-shots act as queries for embedding-based similarity search over big public web-crawled corpora. An instruction-tuned LLM then rewrites the top-k retrieved human-written documents into samples in the target format, guided by a task template and the original examples. The authors evaluate CRAFT on biology, medicine and commonsense QA, plus summarization. Models trained on the generated data match or beat general instruction-tuned LLMs on QA. They surpass a model trained on human-curated summaries by 46 preference points, generalize better out of domain, and beat Self-Instruct and Evol-Instruct. Results stay stable even with weaker seed examples. This makes training data for niche or low-resource tasks much cheaper to obtain, without needing distillation from a larger model.

Key points

  • Pipeline: few-shot examples → top-k document retrieval from an embedding database → LLM augmentation into task samples. No intermediate dataset, separately fine-tuned generator, or teacher-model distillation is needed.
  • Tasks: BioQA, medical QA, commonsense QA, and summarization. Recipe generation, also explored, showed weaker scaling because retrieval relevance drops at larger sizes, yielding vague or mixed-up recipes.
  • Summarization result: +46 preference points over training on human-curated data. CRAFT summaries are more abstractive and prose-like than the extractive, headline-style human references.
  • Baselines: outperforms Self-Instruct and Evol-Instruct overall. Qualitatively, Self-Instruct showed factual errors and ambiguous answer options, and Evol-Instruct often drifted off-task.
  • Release and venue: code, datasets and checkpoints are released at github.com/ziegler-ingo/CRAFT. The paper appeared in TACL vol. 13 (2025), pp. 1693–1721. Its PDF states a CC-BY 4.0 license, which may be worth double-checking against the library's licensing note.

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

 Institutions

🎓University of CopenhagenCopenhagen · Denmark115 works · 15,292 citations🎓Ludwig-Maximilians-Universität MünchenMunich · Germany107 works · 56,478 citations🎓Munich Center for Machine LearningMunich · Germany57 works · 1,094 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 473 works

Cite

Cite as Ingo Ziegler, Abdullatif Köksal, Desmond Elliott and Hinrich Schütze. 2025. CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl.a.56

@article{ziegler2025craft,
  title = {{CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation}},
  author = {Ingo Ziegler, Abdullatif Köksal, Desmond Elliott and Hinrich Schütze},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2025},
  doi = {10.1162/tacl.a.56},
  url = {https://doi.org/10.1162/tacl.a.56}
}

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