Deciding Whether (and How) to Use Weak-Model Synthetic Data for Instruction Fine-Tuning
You lead an internal LLM enablement team building an instruction-following assistant for employees. You have (a) a strong base model you can fine-tune, (b) a small “weak” in-house model that is cheaper to run but noticeably less accurate, and (c) a small set of 500 high-quality, human-written instruction–response examples from your domain. A proposal suggests using a Self-Instruct-style loop to automatically generate 200,000 new instruction–response pairs, but to reduce cost it would use the weak model to (1) generate many candidate instructions and responses and (2) score/filter them before fine-tuning the strong model on the resulting dataset.
Write an evaluation of this proposal that recommends a concrete training-data strategy (you may accept, reject, or modify it). Your answer must explain how instruction fine-tuning objectives interact with: (i) Self-Instruct/automatic data generation, (ii) data selection and filtering, and (iii) weak-to-strong generalization risks when the “teacher” is weak. Include at least three specific filtering/selection criteria you would implement, and explain how each criterion mitigates a particular failure mode (e.g., error amplification, bias reinforcement, mode collapse/repetition, low novelty, misaligned instruction distribution). Conclude with what evidence you would look for in offline evaluation to decide whether the weak-model-generated dataset is helping or harming the strong model’s real employee use cases.
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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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Performance Improvement by Scaling Fine-Tuning Tasks
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Generalization Challenges in Instruction Fine-Tuning
Cost-Effectiveness of Instruction Fine-Tuning for Generalization
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Persistence of General Instruction-Following Behavior After Fine-Tuning
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Suitability of Instruction Fine-Tuning for Well-Defined Tasks
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