Large Language Models (LLMs)

AI systems trained on very large amounts of textual data that can generate human-like text, perform translation, sentiment analysis, and other language tasks. The strategy explicitly defines LLMs as key technology components for both the national foundational model and subsequent domain-specific models.

AI models trained on very large text corpora that can understand, generate, and transform human language in fluent and context-aware ways. NAISR 2.0 highlights LLMs as a key class of models with broad economic and social impact requiring stewardship and integration into sectoral strategies.

Statistical models trained on large text corpora to generate or predict natural language; the Framework identifies LLMs as a prominent subclass of generative AI with specific risks (e.g., hallucinations) and assurance needs for government use.

AI systems trained on very large text corpora to generate, summarise or transform natural language outputs; in this plan they are treated as a distinct application category requiring dedicated knowledge resources and targeted risk assessments (e.g., for hallucinations, privacy and misuse).

Large Language Models (LLMs) are a common subset of generative AI that produce text by probabilistic selection of tokens based on learned patterns; examples referenced include public tools such as ChatGPT and Bard.

A type of artificial intelligence model specifically targeted by Executive Order 14319, which are required to adhere to the Unbiased AI Principles when procured by federal agencies.