A synthetic data generator uses a language model to create new training or test examples, such as questions and answers, instead of collecting them all from people.
In plain words
You start with a few seed examples or a description of what you need. The model writes many new instructions, inputs and outputs. A filtering step then removes broken, low-quality or near-duplicate examples, and the rest become a dataset for fine-tuning or testing. The Self-Instruct paper showed this loop could teach a model to follow instructions with almost no human labelling. Tools such as distilabel help build these pipelines.
A quick example
Imagine building a support bot with little real data. You write 20 sample customer questions, ask a model to produce 2,000 varied ones with answers, filter out duplicates and wrong answers, then use the result to fine-tune and test the bot.
Where you'll meet it
- Instruction tuning and fine-tuning datasets.
- Test sets for classification and extraction tasks.
Related
Sources
- PaperSelf-Instruct: Aligning Language Models with Self-Generated Instructions, Wang et al. (arXiv) · read 29 Sept 2026
- Docsdistilabel, Argilla (GitHub) · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.