Multi-Head Attention

Definitions

1. Multi-Head Attention is a mechanism that weights the most relevant tokens, positions, or features during computation. It is commonly used for transformers and sequence models that need selective context use, where teams need predictable behavior under real workloads rather than toy examples. Practitioners pay attention to memory cost, context length, and quality, because those factors usually determine whether the approach improves quality, latency, reliability, or operating cost in production.

   In plain English: Multi-Head Attention 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 Multi-Head Attention 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