Data quality required for fine-tuning

Preparing fine-tuning data is fundamentally about 'preparing high-quality, representative data aligned with the task, with appropriate labeling.' Data curation and governance (managing rights, privacy, and bias) are important. 'Quality does not matter as long as there is volume,' 'unlabeled is fine,' and 'mix in unrelated data' are all errors that reduce performance.