Negative Sampling

Definitions

1. Negative Sampling is a decoding control that shapes how a generative model selects its next output. It is commonly used for text and multimodal generation systems that trade determinism for diversity, where teams need predictable behavior under real workloads rather than toy examples. Practitioners pay attention to stability, creativity, and repetition, because those factors usually determine whether the approach improves quality, latency, reliability, or operating cost in production.

   In plain English: Negative Sampling is an AI concept teams use to train models, guide predictions, or make model behavior more reliable and easier to control in practice.

   Example: "We evaluated Negative Sampling in the new model pipeline because the baseline was plateauing; once it was wired into training and evaluation, quality improved enough to justify rolling it into the next release."

Related Terms

• Lemmatization
• Linear Layer
• Linear Probe
• Linear Regression
• Llama
• Logistic Regression
• Logit
• Long Short-Term Memory