Self-Instruct › Classification Task Identification
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After generating instructions, the pipeline determines whether each represents a classification task or an open-ended generation task. This distinction matters because the two task types require different instance generation strategies. Classification and generation tasks have fundamentally different output structures, and treating them identically would lead to suboptimal training data.
Classification tasks have a fixed, limited set of valid outputs (like sentiment labels or categories). The outputs for these tasks are constrained to a small vocabulary of options, and the model's job is essentially to select the correct label. Open-ended tasks can have diverse, creative outputs (like writing stories or answering questions). For these tasks, there is no single "correct" answer, and the space of valid responses is large. The model makes this determination through another prompted generation. Think of this classification step as a routing switch: it sends each instruction down one of two paths, each optimized for a different type of output structure.
The need for explicit task type identification reveals an important property of natural language instructions: they do not always make their expected output type obvious. "Evaluate the quality of the given code" could be a classification task (good/bad/needs improvement) or a generation task (write a detailed review). The Self-Instruct framework resolves this ambiguity explicitly by asking the model to classify each instruction before generating instances. This keeps the subsequent generation strategy is appropriate for the task type.
This classification step also has broader implications for the quality of the training data. Classification examples trained without output-first generation tend to be heavily imbalanced. This reflects the natural distribution of examples rather than a balanced cross-section of labels. Generation examples trained with input-first approaches are more coherent because the input provides clear context for the expected output. By routing tasks through type-specific generation strategies, Self-Instruct produces better-calibrated training data than a one-size-fits-all approach would allow.
def create_classification_prompt(instruction):
"""
Prompt to determine if an instruction represents a classification task.
"""
prompt = f"""Determine whether the following task output is a
fixed set of categories or labels (classification) or free-form text (generation).
Task: {instruction}
If this is a classification task, respond with "Classification".
If this is an open-ended generation task, respond with "Generation".
Answer:"""
return prompt# Example classification decisions
test_instructions = [
"Classify the sentiment of the given review as positive, negative, or neutral.",
"Write a short story about a robot learning to paint.",
"Determine if the given statement is a fact or an opinion.",
"Explain the concept of photosynthesis to a 10-year-old.",
]
# In practice, these would be model outputs
classification_labels = [
"Classification",
"Generation",
"Classification",
"Generation",
]Task type classification: [Classification] Classify the sentiment of the given review as positive, nega... [Generation ] Write a short story about a robot learning to paint.... [Classification] Determine if the given statement is a fact or an opinion.... [Generation ] Explain the concept of photosynthesis to a 10-year-old....
The model correctly identifies the nature of each task, distinguishing between those requiring constrained labels (Classification) and those inviting open-ended text (Generation). Notice how the presence of explicit label options in the instruction (like "positive, negative, or neutral") provides a strong signal for classification tasks, while verbs like "write" or "explain" suggest generation tasks.
The classification distinction guides the next stage of the pipeline. Classification tasks benefit from output-first generation (where you first generate possible labels, then create inputs that would warrant each label). This approach ensures balanced representation across all label categories. Generation tasks work better with input-first approaches (where you create a plausible input, then generate an appropriate output). This order is more natural for open-ended tasks where the input context shapes what constitutes a good response.
Edge Cases and Ambiguous Instructions
Not every instruction falls cleanly into the classification or generation category. Some instructions occupy a middle ground: "Rate the difficulty of the math problem on a scale from 1 to 10" has constrained output values but a continuous rather than categorical output space. "Identify all named entities in the passage" is neither a fixed set of labels nor free-form generation but a structured extraction task. These edge cases require additional handling in production pipelines.
The original Self-Instruct paper simplified this ambiguity by treating any task with a fixed, small output space as classification and everything else as generation. This binary split is pragmatically effective even if it is not theoretically precise. A more sophisticated system might distinguish among classification and regression tasks, along with extraction and generation tasks, applying different instance generation strategies to each. But the additional complexity often does not justify the marginal improvement in training data quality, particularly given the other sources of noise in the pipeline.
For practical implementations, the most important refinement is handling tasks where the output space is small but the valid outputs are not enumerable in advance. Question answering tasks, for example, have open-ended outputs but tend to be short and factual. Treating these as generation tasks with input-first instance generation works reasonably well, even though the resulting instances may lack the label balance that output-first generation achieves. The key is to apply the strategy that produces the most coherent instances for the particular instruction type, even if that means using judgment rather than a strict rule.