Choosing a Weak-Model + Self-Instruct Data Strategy for Instruction Fine-Tuning Without Regressions
You lead an applied LLM team at a regulated enterprise building an internal “policy-aware writing assistant” (emails, memos, and customer responses). You have a strong base model you can fine-tune, but only a small set of 800 human-written instruction–response examples (high quality, expensive to expand). To scale, the team proposes a pipeline: (1) use a smaller, cheaper “weak” model to generate 200k instruction–response pairs via a Self-Instruct-style loop (the model generates new instructions, then generates answers), (2) automatically filter the synthetic set, and (3) instruction fine-tune the strong model on the filtered synthetic data plus the 800 human examples. After a pilot run, offline eval shows broader coverage of request types, but two regressions: the model is more confident when wrong on policy questions, and it overuses a single “safe” template response.
As the decision-maker, what specific changes would you make to the data generation + selection/filtering + fine-tuning setup to keep the coverage gains while reducing (a) error amplification from weak supervision and (b) mode-collapse/repetitiveness? In your answer, justify how your changes address the causal mechanism behind each regression and explain at least one tradeoff you are accepting.
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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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