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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Structure of an Instruction Fine-Tuning Sample
Requirement of Fine-Tuning Data for Instruction Following
Performance Improvement by Scaling Fine-Tuning Tasks
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Instruction Fine-Tuning as a Standard Training Process
Engineering Effort in Instruction Fine-Tuning
Cost and Data Limitations of Diverse Instruction Fine-Tuning
Synthetic Data as Supervision Signals in Advanced Fine-Tuning
Implicit Instruction Following via Response-Only Fine-Tuning
Sample Efficiency
Generalization Challenges in Instruction Fine-Tuning
Cost-Effectiveness of Instruction Fine-Tuning for Generalization
Necessity of Further Adaptation for Broad Instruction Following
Scaling Instruction Fine-Tuning for Broader Capabilities
Potential Inefficiency of Scaling Instruction Fine-Tuning for Generalization
Comparison of Fine-Tuning Strategies: Scaled Diversity vs. Efficient Adaptation
Persistence of General Instruction-Following Behavior After Fine-Tuning
Challenge of Finding a Superior Supervisor for Strong LLMs
Definition of Instruction Fine-Tuning
Limited Scope of Fine-Tuning Data for Downstream Tasks
Objective for Distribution Matching in Fine-Tuning
Importance and Demand for Instruction Fine-Tuning Datasets
Methods for Providing Textual Instructions in Fine-Tuning
Improving LLM Generalization by Diversifying Tasks and Instructions
Cost and Effort Comparison: Pre-training vs. Fine-tuning
Suitability of Instruction Fine-Tuning for Well-Defined Tasks
Classification of Instruction Fine-Tuning as an Alignment Problem
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Designing a Synthetic Instruction Fine-Tuning Pipeline Under Budget and Quality Constraints
Deciding Whether (and How) to Use Weak-Model Synthetic Data for Instruction Fine-Tuning
Diagnosing and Fixing a Synthetic Instruction-Tuning Data Flywheel That Degrades Model Behavior
Choosing a Weak-Model + Self-Instruct Data Strategy for Instruction Fine-Tuning Without Regressions
Selecting and Filtering Self-Generated Instruction Data When Bootstrapping a Strong Model from a Weak Supervisor
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