When machine learning models are used in production, "data drift" occurs when the live input text (e.g., customer reviews or social media posts) starts to look different from the data used during training.
In the context of technology and language, often refers to the gradual change in data or meaning over time. Here are a few ways this concept is currently used to "generate" or manage text: 1. Semantic Drift in AI Generation When machine learning models are used in production,
: Tools like Evidently AI use binary classifiers to distinguish between "reference" and "current" data to detect if the text style or content has changed. Semantic Drift in AI Generation : Tools like
Know When To Stop: A Study of Semantic Drift in Text Generation When machine learning models are used in production,
: Swapping the labels of data categories (e.g., making "positive" sentiment act as "negative").
: Monitoring changes in sentence length, word distributions, or the appearance of "Out of Vocabulary" (OOV) words. 3. Generating Drift for Testing
: Graphic designers use "drift" as a visual style, creating drifting typography components or motion graphics that make text appear to slide or float.
