Definition
A large language
model (LLM) is a type of artificial intelligence that is trained on massive
datasets, often billions of words, that can generate human-like language and
perform various language related tasks. ChatGPT, Claude, Microsoft Copilot,
Gemini, and Meta AI are all referred to as LLMs and can handle a wide range of
language tasks like answering questions, summarizing text, translating between
languages, and writing content. By processing vast dataset, the LLM learns
patterns and rules of language, much like a human learns to communicate through
exposure to language.
Some language models are called “foundation models” due to their multimodal nature (i.e. they can work with other media types that are not text). Foundational models are models that are trained on vast amounts of data and perform a wide range of tasks and operations not related to human language [1].
For our better understanding of LLMs, an example of an LLM is an AI that can quickly translate Federal Ministry announcements from English into Hausa, Yoruba, and Igbo without losing official tone and policy context, making them ideal for nationwide public information campaigns. Unlike traditional translation tools that might miss cultural contexts like honorifics or respectful addressing conventions, LLMs can adapt to regional communication styles—knowing "Oga" for authority figures or proper Hausa greetings—with minimal configuration, ensuring government health advisories, tax policies, and electoral information reach all citizens in their preferred languages.
A large language model [2].
Origin
The origins of AI
language models can be traced back to the early days of AI. The Eliza language
model is an example of early AI language model introduced in 1966 at MIT.
Modern LLMs
emerged in 2017 and use transformer models, which are neural networks commonly
called transformers. With a large number of parameters and the transformer
model, LLMs can understand and produce quick accurate responses, which makes
the AI technology applicable broadly across many different domains.
In 2021, Stanford Institute for Human-Centered Artificial Intelligence coined the term foundational models, which is used to refer to some LLMs which are so large and impactful that it serves as the foundation for further optimizations and specific use cases [3].
Context and
Usage
LLMs help users produce
coherent, contextually appropriate sentences and paragraphs as response to a
given prompt. They have a wide range of applications that cuts across different
domains, enabling businesses automate tasks. Some of them are as follows:
- Customer service: LLMs are utilized across industries to serve customer purposes, in form of chatbots or conversational AI.
- Banking: LLMs, through processing financial transactions and customer communications, help detect potential fraud, usually as part of wider fraud detection systems.
- Legal: large language models aid lawyers, paralegals, and legal staff with tasks such as navigating through massive legal datasets and drafting legal documents,.
- Healthcare and science: Textual data connected to proteins, molecules, DNA, and RNA, can be processed by large language models, aiding in research, development of vaccines, discovery of potential cures for diseases, and improving preventative care medicines. In addition, LLMs are also utilized as medical chatbots for patient intakes or basic diagnoses that need human supervision.
- Marketing: LLMs help marketing teams in sentiment analysis, content generation, and brainstorming campaign ideas, assisting to produce text for pitches, advertisements, and other materials.
- Tech: Large language models are employed in various tasks like helping developers write code and improving search engine query responses [4].
Why it Matters
LLMs are behind
the boom of artificial intelligence giant strides since the late 2010s and
2020s. They stand as a symbol of progress in natural language processing (NLP)
and machine learning decades of research.
Unlike traditional search engines and other programmed systems that utilize algorithms to match keywords, LLMs signifies a big step forward in how humans interact with technology as the first AI system that can handle unstructured human language at scale, allowing for natural communication with machines, understanding deeper context, nuance and reasoning. After undergoing training, LLMs, can be deployed in various applications that include interpreting text, summarizing an article, debugging code or drafting a legal clause. When given agentic capabilities, LLMs can perform, with varying degrees of autonomy, various tasks that would otherwise be performed by humans [5].
Related AI Models and Architectures
- Latent Space: Abstract mathematical space where AI models represent data in compressed, meaningful dimensions
- Mixture of Experts: Architecture that uses multiple specialized sub-models coordinated by a gating network
- Model: Mathematical representation that learns patterns from data to make predictions or decisions
- Neural Network: Computing system inspired by biological neural networks that learns patterns from data
- Neural Radiance Fields (NeRF): AI technique for creating photorealistic 3D scenes from 2D images
In Practice
Gemini is a good example of a real-life case study of LLMs in action. As a multimodal model, it processes and generates both text and images. It does a good job with creative and analytical tasks that require complex contextual understanding.
References
- University of Arizona. (2023). What is a large language model (LLM)?
- Geeksforgeeks. (2025). What is a Large Language Model (LLM).
- Kirvan, P., Kerner, S., M. (2025). What is a large language model (LLM)?
- Elastic. (2025). What are large language models (LLMs)?
- Stryker, C. (n.d). What are large language models (LLMs)?