Large Language Model (LLM)
Definition
An LLM is a language model that generates responses from patterns in many texts.
A large language model is trained on extensive data and processes language based on learned statistical patterns. During text generation, it selects successive tokens. Liquid outputs can be factually wrong; the task and model variant determine the suitability.
Simply explained
Like a very large text workshop: it formulates, but has to check facts.
Why it matters in practice
Helps to realistically classify the possibilities, quality and limitations of AI systems.
Example from business
Possible example: A team has a draft for an internal guide created and checks every technical statement.
Limits and pitfalls
- Confuse fluent language with reliable facts; enter confidential data unchecked.
Origin & history
The GPT-3 work by Brown and co-authors (2020) is a documented research milestone for large language models. She also examines Few-Shot tasks without further task-specific training.
Use in mid-sized companies
- Possible application in small and medium-sized enterprises: A team has a draft for internal guidance created and checks every technical statement.
Frequently asked questions
What does Great Language Model (LLM) mean?
An LLM is a language model that generates responses from patterns in many texts.
What errors should be avoided?
Confuse fluent language with reliable facts; enter confidential data unchecked.
Sources
- Language Models are Few-Shot Learners · Brown et al. (2020), Language Models are Few-Shot Learners
- Generative AI Profile; Grenzen der faktischen Zuverlässigkeit · NIST (2024), Generative AI Profile; Grenzen der faktischen Zuverlässigkeit
Sources checked on 09.10.2026. Editorial use cases are hypothetical; no evidence of real customer results.
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