Enhancing LLM Fine-Tuning with Synthetic Data
With a foundation in using synthetic data for LLM pretraining, we now shift our attention to fine-tuning. This stage adapts general-purpose LLMs for specific tasks, improved instruction adherence, or distinct operational behaviors. Synthetic data provides a valuable resource for creating the targeted datasets required for effective fine-tuning, particularly when real-world data for specialized needs is insufficient or unavailable.
This chapter covers how to:
- Employ synthetically generated data for instruction fine-tuning (IFT), enabling LLMs to better understand and follow directives.
- Implement methods like Self-Instruct to construct diverse fine-tuning datasets.
- Produce synthetic examples to aid LLM performance in few-shot or zero-shot learning situations.
- Structure synthetic data in suitable formats (e.g., JSONL) for various fine-tuning pipelines.
- Utilize synthetic inputs to influence model characteristics, such as writing style or persona.
- Engage in a practical exercise by developing a synthetic dataset tailored for a specific fine-tuning goal.
Sections
4.1 Instruction Following Fine-Tuning using Generated Data
4.2 Crafting Effective Instruction-Response Pairs Synthetically
4.3 Methods for Building Diverse Fine-Tuning Datasets
4.4 Generating Data for Few-Shot and Zero-Shot Learning Scenarios
4.5 Structuring Data for Various Fine-Tuning Frameworks
4.6 Shaping Model Behavior (Style, Persona) via Synthetic Inputs
4.7 Hands-on Practical: Creating a Synthetic Dataset for Task-Specific Fine-Tuning
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