What is Data Labeling?
TL;DR
The process of assigning labels (ground truth data) to data for supervised AI model training.
Data Labeling: Definition & Explanation
Data Labeling is the process of assigning correct labels (annotations) to data for supervised learning of AI models. This includes tagging images with labels like 'cat' or 'dog,' classifying text sentiment as 'positive' or 'negative,' and transcribing audio to text. Model performance is directly tied to label quality, following the GIGO (Garbage In, Garbage Out) principle. Labeling traditionally requires significant human effort, making it costly and time-consuming. Recent developments include semi-automated labeling using AI (Active Learning), label generation using LLMs (AI-assisted Labeling), and distributed labeling via crowdsourcing platforms (Amazon Mechanical Turk, etc.). RLHF (Reinforcement Learning from Human Feedback) is also a form of labeling that significantly contributed to ChatGPT's quality improvement.