Large language models (LLMs) are deep learning models designed to process and generate human-like language. They are capable of text generation, translation, sentiment analysis, information retrieval, and tasks such as summarization, question answering, and code generation. These models form the foundation of many popular AI tools, like ChatGPT and Google’s Gemini.

LLMs are part of natural language processing (NLP), which focuses on enabling computers to analyze and generate human language. While earlier NLP systems were built for specific tasks like translation or simple question-answering, modern LLMs can perform a broad range of language tasks without being specifically programmed for each one.

These models are trained on vast collections of text from books, websites, forums, and other publicly available or proprietary sources. Through pre-training – a process where the model analyzes large amounts of text to learn statistical patterns in language – they develop the ability to predict words, structure sentences, and generate coherent text. As a result, they can generate text that appears human-like when given a prompt or question.

The ‘large’ in large language models refers to their scale, specifically the number of parameters they contain. Parameters – adjustable numerical values in a neural network – help the model recognize patterns, make predictions, and generate text. These models can have millions, billions, or even trillions of parameters. Generally, models with more parameters can capture more complex language patterns, though performance also depends on training data quality and model architecture. 

How do large language models work?

LLMs function through a sophisticated process of training, text processing, and pattern recognition. Understanding their fundamental mechanics helps explain both their capabilities and limitations in generating human-like text.

The training process

Creating an LLM involves two main stages of development:

  • Pre-training: First, the model learns general language patterns by analyzing enormous amounts of text data. During this stage, the model identifies patterns in grammar, facts, reasoning, and cultural references, all without direct instruction on what to learn. The process is like reading thousands of books, but instead of understanding the meaning, the model detects patterns in how words and ideas are used together.
  • Fine-tuning: After pre-training, the model undergoes additional training to make it more helpful, accurate, and safe for specific applications. The process involves human feedback to reduce errors and inappropriate responses.

Breaking down text

An LLM processes text by breaking it down into smaller pieces called tokens. Depending on the model’s tokenization method, a token might be a full word, part of a word, or even a single character. 

For example, a complex word like ‘unforgettable’ might be separated into ‘un,’ ‘forget,’ and ‘able’ as individual tokens, while simple common words like ‘it’ or ‘is’ would remain whole.

Tokenization helps the model work efficiently with language, similar to how human readers recognize familiar word patterns rather than processing each letter individually.

Context windows

One important limitation of large language models is their context window, which determines how much text they can process at once.

Early models could handle only a few hundred to a few thousand tokens – roughly 1–2 pages of text – before losing context. In contrast, newer models can manage tens or even hundreds of thousands of tokens, enabling them to process much longer documents, sometimes exceeding 100 pages.

When input exceeds this limit, the model may lose track of earlier details, leading to inconsistencies in longer conversations or documents.

Generating responses

When creating text, LLMs work by predicting what word or token should come next based on all the previous text:

  1. The model receives input text (a prompt or question).
  2. It processes the text to identify relevant patterns and context.
  3. Based on patterns learned during training, it predicts what should come next.
  4. It adds this prediction to the text and repeats the process.
  5. This continues until it generates a complete response.

A setting called “temperature” can be used to adjust the model to produce different types of responses. A lower temperature produces more predictable, focused answers, while a higher temperature produces more varied, creative outputs.

Multimodal capabilities

While traditional LLMs work only with text, newer “multimodal” models can analyze and respond to multiple types of input, making them more versatile and capable.

  • Processing images: Some multimodal models can interpret pictures, describe what they see, and answer questions about visual content.
  • Working with audio: Certain models can process spoken language, recognize sounds, and generate relevant responses.
  • Creating visual content: Advanced models can generate simple images based on text description, though specialized systems like DALL·E typically handle this. 

Expanding beyond text, these models better mimic how humans communicate, using both language and visual cues. For example, instead of describing a problem in words, you could show a multimodal model a photo of a bicycle with a flat tire and ask, “What’s wrong, and how do I fix it?”

What are large language models used for?

Large language models’ ability to process and generate human-like language makes them versatile tools for both personal and professional use.

However, enterprise adoption is still in its early stages. According to Deloitte research, only 20%-25% of surveyed organizations have integrated or experimented with LLM-based AI systems.

Here are some common applications of large language models:

  • Digital assistants: Virtual assistants like ChatGPT use LLMs to interpret requests and provide helpful responses across a wide range of topics.
  • Writing support: LLMs assist with drafting emails, summarizing long documents, and improving written content.
  • Search enhancement: Modern search engines leverage LLMs to better interpret queries and deliver more relevant results.
  • Language translation: LLMs power translation services that more accurately capture meaning than previous models.
  • Content creation: Businesses use LLMs to generate website content, product descriptions, and marketing materials.