Thinking about High-Quality Human Data

The article discusses the critical importance of high-quality human-labeled data for training modern deep learning models, particularly for classification tasks and RLHF labeling used in LLM alignment. Despite the recognized value of quality data, there's a notable preference in the ML community for model development work over data collection and annotation work.

• →High-quality human-annotated data is essential fuel for modern deep learning model training.
• →Most task-specific labeled data comes from human annotation for classification and RLHF labeling.
• →The ML community recognizes data quality importance but shows preference for model work over data work.
• →Human data collection requires careful attention to detail and precise execution.
• →RLHF labeling for LLM alignment can be structured as classification tasks.