Interactive data generation and quality assurance programs
Kiln provides a closed-loop data optimization tool chain:
- template engine: Built-in data templates for legal/medical/e-commerce and 20+ other domains to ensure the quality of the foundation
- restrictive rule: Validation rules can be set for numeric range/string format/logical relationship, etc.
- Real-time preview: Generate data and present model predictions to identify problematic samples in a timely manner
- Enhanced Strategy: Support for adding data enhancements such as controlled noise, semantic scrambling, etc.
Key Tips: 1) Use the "Data Diffusion" function to automatically expand similar samples; 2) Convert model error cases into quality training data through "Cue Inversion". It is recommended to maintain a 1:3 ratio between the amount of generated data and the actual data.
This answer comes from the articleKiln: Simple LLM model fine-tuning and data synthesis tool, 0 code base to fine-tune your own small modelsThe
May not be reproduced without permission:AI productivity tools " How can synthetic data quality be optimized to enhance model fine-tuning?
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