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Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

Paper · 2024 · Haoran Li, Qingxiu Dong, Zhengyang Tang et al.

Summary

Instruction-tuning data for LLMs is usually derived from seed prompts or existing datasets (FLAN, Self-Instruct, Evol-Instruct), so its topical range is limited by those starting points. GLAN removes this dependency. Its only input is a taxonomy of human knowledge and skills, drafted with GPT-4 and checked by humans, which divides knowledge into fields, then sub-fields, then disciplines, covering both academic and vocational areas. For each discipline, GPT-4 lists subjects and writes a syllabus per subject, split into class sessions and key concepts. It then creates homework-style questions from randomly combined sessions and concepts. GPT-4 or GPT-3.5 runs this fully automated stage. A Mistral-based 7B model tuned on the resulting data improved in math, coding, exam-style questions, logic tasks and general instruction following, without any benchmark-specific training data. The method matters because it scales cheaply and stays domain-general. It can also be extended by attaching a new taxonomy node and generating data only for that node, which could help cover under-represented domains or languages.

Key points

  • The pipeline runs taxonomy → subjects → syllabus → class sessions and key concepts → synthetic instructions. Only the taxonomy step involves human verification, which is cheap because it contains relatively few nodes.
  • The authors introduce a held-out benchmark, GLAN-Test, with 6,300 instructions across 126 disciplines (50 per discipline), grouped into 8 academic and industry fields. It is evaluated with GPT-4 pairwise scoring.
  • On GLAN-Test, the GLAN 7B model's average GPT-4 score gap is +1.61 vs Orca2-7B, +0.43 vs Mistral-7B-Instruct and +0.19 vs WizardLM-13B-V1.2. It still trails GPT-4 by −0.55.
  • Other evaluations include IFEval and the Evol-Instruct test set. The model is weaker in disciplines such as American history, Divinity and Radiology.
  • Current data is mostly single-turn question–answer pairs. Multi-turn dialogues and long documents are planned as future work.

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, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,894 works

Cite

Cite as Haoran Li, Qingxiu Dong, Zhengyang Tang, Chaojun Wang, Xingxing Zhang, Haoyang Huang, Shaohan Huang, Xiaolong Huang, Zeqiang Huang, Dongdong Zhang, Yuxian Gu, Xin Cheng, Xun Wang, Si-Qing Chen, Li Dong, Wei Lu, Zhifang Sui, Benyou Wang, Wai Lam and Furu Wei. 2024. Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models. arXiv:2402.13064.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2402.13064 · arXiv 2402.13064

@misc{li2024synthetic,
  title = {{Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models}},
  author = {Haoran Li, Qingxiu Dong, Zhengyang Tang, Chaojun Wang, Xingxing Zhang, Haoyang Huang, Shaohan Huang, Xiaolong Huang, Zeqiang Huang, Dongdong Zhang, Yuxian Gu, Xin Cheng, Xun Wang, Si-Qing Chen, Li Dong, Wei Lu, Zhifang Sui, Benyou Wang, Wai Lam and Furu Wei},
  year = {2024},
  doi = {10.48550/arxiv.2402.13064},
  eprint = {2402.13064},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2402.13064}
}

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