Self-Instruct › Seed Task Design
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The quality of Self-Instruct outputs depends critically on the initial seed tasks. These human-written examples establish formatting and diversity expectations as well as quality standards that the model will follow when generating new tasks. Think of seed tasks as the DNA of the dataset: they carry the basic blueprint that all subsequent generations inherit and recombine. Poor seed design leads to limited, repetitive outputs, while thoughtful seed curation enables the generation of rich, diverse datasets that cover a wide range of task types and subject areas.
The seed tasks perform several functions simultaneously. They demonstrate the desired format, showing the model what a well-formed instruction-input-output triple looks like. They establish quality standards, giving the model examples of the level of specificity and coherence expected in each field. And they introduce topical diversity. This keeps the model's initial generation attempts span multiple domains rather than clustering around a single subject area. Because the model cannot generate task types it has never seen demonstrated, the seeds act as templates for the entire generation process.
Designing effective seeds requires thinking carefully about coverage across multiple dimensions. A seed set that contains only text classification tasks will produce a dataset heavily weighted toward classification. A seed set that contains only short-output tasks will fail to demonstrate the formatting conventions for longer, structured outputs. The original Self-Instruct paper addressed this by manually curating 175 seeds to span a broad range of task categories, output lengths, and topic areas. This investment in seed quality paid dividends throughout the generation process, as the diverse demonstrations guided the model toward similarly varied outputs.
The relationship between seed quality and generation quality is not merely correlational. In the in-context learning regime, the model explicitly uses the seed demonstrations to identify patterns and generate continuations. If all demonstrated instructions start with "Classify," the model will generate instructions that start with "Classify." If seeds include a mix of verbs ("Write," "Explain," "Translate," "Identify," "Compare"), the model will diversify its generated instruction vocabulary accordingly. This direct causal link between seed design and output variety makes seed curation one of the highest-impact activities in the entire Self-Instruct workflow.
The original Self-Instruct paper used 175 seed tasks, each containing an instruction, optional input, and expected output. These seeds were designed to cover a broad range of task types, so the model had exposure to many different patterns from the very beginning:
# Example seed tasks spanning different categories
seed_tasks = [
{
"instruction": "Classify the sentiment of the given movie review.",
"input": "This film was absolutely wonderful. The acting was superb!",
"output": "Positive",
},
{
"instruction": "Write a haiku about autumn.",
"input": "",
"output": "Leaves drift slowly down\nCrisp air fills the empty woods\nNature's final bow",
},
{
"instruction": "Convert the following temperature from Celsius to Fahrenheit.",
"input": "25 degrees Celsius",
"output": "77 degrees Fahrenheit",
},
{
"instruction": "List three potential causes of the given historical event.",
"input": "The French Revolution",
"output": "1. Economic crisis and heavy taxation of the poor\n2. Social inequality between estates\n3. Enlightenment ideas spreading democratic principles",
},
{
"instruction": "Rewrite the sentence to make it more formal.",
"input": "Hey, can you help me out with this thing?",
"output": "Would you be able to assist me with this matter?",
},
]The seed tasks should exhibit several key properties that together establish a strong foundation for generation.
- Format diversity: Mix of tasks with and without inputs, varying output lengths and structures. Some tasks require only an instruction, while others need additional context provided as input.
- Topic coverage: Span different domains like science, arts, daily life, and abstract reasoning. This breadth ensures the model generates instructions across many subject areas rather than clustering around a single theme.
- Task type variety: Include classification and generation tasks, along with transformation and reasoning tasks. Each task type exercises different capabilities and produces different output patterns.
- Clear patterns: Demonstrate the expected instruction-input-output format unambiguously. The model should have no confusion about what each field contains or how they relate to one another.
The key insight is that seeds function as active teachers, not merely examples. Every seed simultaneously teaches the model what format to follow, what topics are in scope, and what level of detail is appropriate. Spending extra time crafting high-quality seeds with broad coverage pays compounding returns as the pipeline scales.
