AWS has outlined essential steps for preparing data for supervised fine-tuning (SFT) of foundation models, highlighting the importance of quality checks and formatting to ensure effective model training. The process begins with auditing raw data before investing in formatting or training infrastructure, as early detection of issues can save significant time and computational resources. The post emphasizes that accuracy and correctness are paramount, with examples needing to be gold-standard answers suitable for production deployment. Incorrect examples can lead to persistent bad habits in the model, making quality a critical factor over quantity. LIMA demonstrated that 1,000 well-curated examples can match models trained on much larger datasets, while AlpaGasus showed that filtering an instruction set to its cleanest 20 percent can result in faster training and higher scores.
Dataset diversity is identified as one of the strongest predictors of SFT success, with research highlighting two key properties: semantic coverage and information depth. Semantic coverage refers to the breadth of task domains and prompt phrasings, while information depth relates to the richness of individual examples. A dataset covering only a narrow slice of production traffic may result in a model that performs well on that slice but poorly elsewhere. The post recommends auditing datasets for prompt variation, domain and topic breadth, difficulty range, and edge cases. A practical approach involves clustering examples by embedding similarity to identify gaps in training signals. For instance, if a customer-support dataset lacks examples for refund-related queries, those gaps should be addressed before training.
The post also discusses the importance of consistency within similar tasks, noting that inconsistent examples can send contradictory signals about correct behavior. It highlights the need for target behavior in training data, such as using consistent labels and responses. Deduplication is recommended to prevent overfitting on patterns, especially when merging data from multiple sources. Additionally, the post emphasizes the need to scan datasets for harmful, biased, or inappropriate content, using automated classifiers like Llama Guard to flag problematic examples for human review. Finally, the post mentions that formatting is not just about syntax but also shapes the model’s learned behavior, as SFT teaches the model to respond to a specific input structure.
Source: awsml
