Causal Language Model

Definitions

1. A language model trained to predict the next token using only preceding context. It influences how models are trained, evaluated, or served, and it can materially change accuracy, robustness, latency, cost, or interpretability. Practitioners usually track it alongside data quality, compute limits, and validation results when moving models into production.

   In plain English: Causal Language Model is an AI concept that affects how a model learns, predicts, or gets deployed. It matters because it changes quality, speed, or reliability.

   Example: "We revisited Causal Language Model during model evaluation because the first run looked fine offline but behaved poorly in production, and the adjustment improved quality without breaking our latency or compute budget."

Related Terms

• Token
• BERT
• LLM Agent
• LLM Evaluation
• LLM Fine-Tuning
• LLM Inference
• LLM Routing
• LLM Safety
• Local LLM