Instruction Data Creation › Seed Task Expansion

Seed task expansion bridges the gap between expensive human annotation and mechanical template conversion. The approach starts with a small set of carefully crafted seed examples, then uses various techniques to expand this seed into a larger, more diverse dataset. This method preserves the quality and naturalness of human-written examples while achieving scale through systematic augmentation. Think of it as using master-crafted templates as a starting point and then having skilled craftspeople produce variations that respect the spirit of the original.

The motivation for seed expansion comes directly from the limitations we identified in both human annotation and template conversion. Human annotation produces excellent examples but at prohibitive cost. Template conversion produces many examples but lacks naturalness and creative breadth. Seed expansion attempts to get the best of both approaches: by starting from human-quality seed examples and expanding them through principled techniques, we can build datasets that are larger than pure human annotation can produce while retaining more naturalness than pure template conversion.

The key insight in seed expansion is that a well-chosen example encodes information about an entire class of similar examples. A seed that demonstrates how to answer a question about a historical event implicitly teaches the model about the structure of historical questions more generally. A seed that demonstrates creative brainstorming implies a space of similar creative tasks. By expanding from carefully designed seeds, we can efficiently populate the space of possible instructions without needing to enumerate every possible instance explicitly.

A good seed set is small but carefully curated. The philosophy behind seed expansion recognizes that a few excellent examples, chosen to cover the space of possible instructions, can serve as anchors for generating many more examples. The key is ensuring that the seeds represent the diversity you want in the final dataset. A seed set that covers only question answering and summarization will produce expansions that cover only those tasks, no matter how many expansion techniques you apply.

A well-designed seed set should demonstrate the following properties:

• Task diversity: Including examples from many task categories (creative writing, question answering, coding, analysis, etc.) ensures that expansion can generate examples across the full range of desired capabilities.
• Format diversity: Showing different input/output structures (single-turn, multi-turn, with/without context) teaches the model that instructions can take many forms.
• Complexity range: Spanning from simple lookups to multi-step reasoning ensures the model learns to handle both easy and difficult requests.
• Style variety: Demonstrating different appropriate tones and verbosity levels teaches the model to adapt its communication style to different contexts.

The Self-Instruct paper, which the next chapter explores in detail, started with just 175 seed tasks. Each seed was written to be distinct, covering a different aspect of instruction-following capability. This small but diverse seed set enabled the generation of tens of thousands of additional examples while maintaining quality and coverage. The success of Self-Instruct demonstrated that the seed set, not the expansion volume, is the critical determinant of final dataset quality. Expanding from 175 excellent seeds produced better results than expanding from 500 mediocre seeds.

Designing seed sets is itself a creative challenge that rewards careful thought. A practical approach is to enumerate the capabilities you want the final model to have, then write seeds that clearly demonstrate each capability. Creative writing, factual question answering, logical reasoning, code generation, summarization, translation, advice-giving, and task decomposition are examples of capabilities worth explicit seed coverage. Within each capability, seeds should cover different topics and difficulty levels, with varied response lengths to signal the range that expansion should produce.

Once you have a seed set, several techniques can expand it without requiring additional human annotation. These techniques use the structure and content of existing examples to generate new variations that preserve the essential characteristics while adding diversity. The goal is to fill in the space around each seed example, populating the neighborhood of each capability cluster with many closely related examples so the model encounters that capability in many forms.

Paraphrasing generates alternative wordings for existing instructions. The goal is to teach the model that the same task can be expressed in many different ways. You might say "Write a poem" or "Compose a poem" or "Create a poem," and the model should recognize these as equivalent requests. Paraphrasing is particularly valuable for instructions that users are likely to phrase in many different ways, such as creative writing tasks or general knowledge questions. When paraphrasing is done by another language model rather than through rules, it can produce remarkably natural variations that capture stylistic subtleties a rule-based system would miss.

The coding variations demonstrate a pattern common in real instruction datasets: a single structural template ("Write a Python function that...") applied to many different problems. Each resulting instruction is distinct and useful as a training example, but the cost of creation is minimal because the structure is reused. The creative writing variations work similarly, substituting different topics into a fixed poetic form. For both types, the variation is in the content, not the format, so the model learns the task structure from the seed and content generalization from the variations.

A third augmentation technique is complexity scaling, where we take simple seed examples and create more complex versions by adding constraints, combining multiple sub-tasks, or requiring multi-step reasoning. A simple "Summarize this article in one sentence" can be scaled to "Summarize this article in one sentence, then identify the three most important supporting points." A simple "Classify this email as spam or not spam" can be scaled to "Classify this email as spam or not spam, explain your reasoning, and suggest how to improve the spam filter to catch similar messages." Complexity scaling ensures the dataset covers the full range of difficulty that users might require.

When expanding from seeds, maintaining diversity matters. Without careful sampling, expansion tends to produce many similar examples clustered around popular seed patterns while neglecting rarer but important task types. This clustering reduces the effective size of the dataset and can lead to models that perform well on common tasks but poorly on less frequent ones. The problem is analogous to class imbalance in supervised learning: if 80% of your training examples are sentiment classification tasks, the model will be very good at sentiment classification but mediocre at everything else.

Diversity sampling techniques help ensure balanced coverage across task types and linguistic patterns. The goal is not to produce equal numbers of every type, but to prevent any type from becoming so dominant that it crowds out others. A dataset that covers many task types with adequate representation for each produces more capable, general-purpose models than one that over-specializes in any particular direction.

The key diversity mechanisms used in practice include:

• Category balancing: Ensuring expanded data maintains representation across all task categories prevents any single category from dominating the training signal. This often requires actively sampling underrepresented categories more heavily during expansion.
• Embedding-based filtering: Using text embeddings to detect and remove examples too similar to existing ones keeps the dataset diverse at a semantic level, catching redundancy that surface-level metrics would miss.
• Verb/topic tracking: Monitoring the distribution of instruction verbs and topics to prevent overconcentration helps identify when expansion is gravitating toward particular patterns that need to be counterbalanced.