Selecting and Filtering Self-Generated Instruction Data When Bootstrapping a Strong Model from a Weak Supervisor
You lead an internal team fine-tuning a pre-trained LLM into a customer-support assistant for your company’s enterprise software. You have only 1,000 human-written, high-quality instruction–response examples (covering tone, policy, and product accuracy). To scale, you consider two synthetic data sources:
A) Self-Instruct expansion: use a strong off-the-shelf LLM to generate new instructions plus responses from your 1,000 seeds, producing 200,000 instruction–response pairs.
B) Weak-to-strong bootstrapping: use your current small in-house model (known to be polite but sometimes wrong on product details) to generate responses for 200,000 automatically generated instructions, then fine-tune your strong target model to match those responses.
After a pilot run, you observe: (1) the fine-tuned model is more compliant with formatting and tone, (2) it is noticeably more confident in a few recurring incorrect product claims that match the small in-house model’s mistakes, and (3) adding more synthetic data without filtering makes these incorrect claims more frequent.
As the person accountable for the next iteration, propose a concrete data strategy (what to generate, what to keep/remove, and what to prioritize) that uses instruction fine-tuning effectively while managing the trade-off between scaling via automatic/self-generated data and the risk of inheriting weak-model errors. Your answer must explicitly explain how your selection/filtering choices change the influence of Self-Instruct data vs weak-model-labeled data on the final model’s behavior.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.4 Alignment - Foundations of Large Language Models
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A development team starts with a large, pre-trained language model that has a broad understanding of language but no specific ability to act as a specialized assistant. To create a helpful summarization tool, they prepare a dataset of several thousand examples, where each example consists of a long article (the instruction) and a concise, accurate summary (the desired response). They then continue training the model on this new dataset for a short period. Which statement best analyzes the primary purpose and effect of this training process?
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