High-quality data is the fuel for modern data deep learning model training. Most of task-specific labeled data comes from human annotation, such as classification task or RLHF labeling (which can be constructed as classification format) for LLM alignment training. Lots of ML techniques in the post can help with data quality, but fundamentally human data collection involves attention to details and careful execution. The community knows the value of high quality data, but somehow we have this subtle impression that “Everyone wants to do the model work, not the data work” (Sambasivan et al.

Lilian Weng is a machine learning researcher and writer who shares insights, research findings, and tutorials on machine learning, artificial intelligence, and data science. Through articles, blog posts, and research summaries, Lilian Weng explores topics such as deep learning, natural language processing, and reinforcement learning. Readers can learn about state-of-the-art algorithms, practical applications of machine learning, and trends shaping the field of AI.

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High-quality data is essential for deep learning model training. Human data collection involves attention to details and careful execution. The wisdom of the crowd can be used to gather diverse perspectives. Rater agreement can be measured using various methods. Different paradigms exist for data annotation. Methods like influence functions and prediction changes during training can help identify potentially incorrect labels.

Thinking about High-Quality Human Data