👉 A label fluid, also known as a dynamic label or adaptive label, is a concept in machine learning and natural language processing that refers to a mechanism where the labels assigned to data points are not fixed but can change based on context, user interaction, or other dynamic factors. Unlike static labels, which are assigned once and remain constant throughout the model's training and inference phases, label fluids adjust in real-time to provide more accurate or contextually relevant labels. This approach is particularly useful in scenarios where the data distribution shifts over time, or when user feedback can help refine and improve label accuracy. By allowing labels to evolve, label fluids enhance the adaptability and performance of machine learning models, especially in applications like sentiment analysis, where the context can significantly influence the interpretation of text.
