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.
Know When To Stop: A Study of Semantic Drift in Text Generation When machine learning models are used in production,
: Tools like Flow can generate scenes of cars drifting, often combined with text prompts to create stylized cinematic effects. Recent studies, such as the Meta AI research,
: Graphic designers use "drift" as a visual style, creating drifting typography components or motion graphics that make text appear to slide or float. Monitoring and Detecting Data Drift
Recent studies, such as the Meta AI research, have identified "semantic drift" as a phenomenon where Large Language Models (LLMs) start a response with correct facts but eventually "drift away" into hallucinations or irrelevant content. To counter this, developers use methods to halt generation before the text loses accuracy. 2. Monitoring and Detecting Data Drift