What is LLM(Large Language Models)?

 What are Large Language Models?

Large language models (LLMs) are a category of deep learning models trained on immense amounts of data, making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks. LLMs are built on a type of neural network architecture called a transformer which excels at handling sequences of words and capturing patterns in text.

LLMs work as giant statistical prediction machines that repeatedly predict the next word in a sequence. They learn patterns in their text and generate language that follows those patterns.

LLMs represent a major leap in how humans interact with technology because they are the first AI system that can handle unstructured human language at scale, allowing for natural communication with machines. Where traditional search engines and and other programmed systems used algorithms to match keywords, LLMs capture deeper context, nuance and reasoning. LLMs, once trained, can adapt to many applications that involve interpreting text, like 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.

LLMs are the culmination of decades of progress in natural language processing (NLP) and machine learning research, and their development is largely responsible for the explosion of artificial intelligence advancements across the late 2010s and 2020s. Popular LLMs have become household names, bringing generative AI to the forefront of the public interest. LLMs are also used widely in enterprises, with organizations investing heavily across numerous business functions and use cases.

LLMs are easily accessible to the public through interfaces like Anthropic’s Claude, Open AI’s ChatGPT, Microsoft’s Copilot, Meta’s Llama models, and Google’s Gemini assistant, along with its BERT and PaLM models. IBM maintains a Granite model series on watsonx.ai, which has become the generative AI backbone for other IBM products like watsonx Assistant and watsonx Orchestrate.

How do large language models work?

LLMs form an understanding of language using a method referred to as unsupervised learning. This process involves providing a machine learning model with data sets–hundreds of billions of words and phrases–to study and learn by example. This unsupervised learning phase of pretraining is a fundamental step in the development of LLMs like ChatGPT (Generative Pre-Trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers). 

In other words, even without explicit human instructions, the computer is able to draw information from the data, create connections, and “learn” about language. This is called AI inference. As the model learns about the patterns from which the words are strung together, it can make predictions about how sentences should be structured, based on probability. The end result is a model that is able to capture intricate relationships between words and sentences. 

LMMs require lots of resources

Because they are constantly calculating probabilities to find connections, LLMs require significant computational resources. One of the resources they draw computing power from are graphics processing units (GPUs). A GPU is a specialized piece of hardware designed to handle complex parallel processing tasks, making it perfect for ML and deep learning models that require lots of calculations, like an LLM.

If you are tight on resources, LoRA and QLoRA are resource-efficient fine-tuning techniques that can help users optimize their time and compute resources.

Certain techniques can help  compress your models to optimize for speed, without sacrificing accuracy.

LLMs and transformers

GPUs are also instrumental in accelerating the training and operation of transformers–a type of software architecture specifically designed for NLP tasks that most LLMs implement. Transformers are fundamental building blocks for popular LLM foundation models such as ChatGPT, Claude, and Gemini.

A transformer architecture enhances the capability of a machine learning model by efficiently capturing contextual relationships and dependencies between elements in a sequence of data, such as words in a sentence. It achieves this by employing self-attention mechanisms–also known as parameters–that enable the model to weigh the importance of different elements in the sequence, improving its understanding and performance. Parameters define boundaries, and boundaries are critical for making sense of the enormous amount of data that deep learning algorithms must process.

Transformer architecture involves millions or billions of parameters, which enable it to capture intricate language patterns and nuances. In fact, the term “large” in “large language model” refers to the extensive number of parameters necessary to operate an LLM.

LLMs and deep learning

The transformers and parameters that help guide the process of unsupervised learning with an LLM are part of a more broad structure referred to as deep learning. Deep learning is an artificial intelligence technique that teaches computers to process data using an algorithm inspired by the human brain. Also known as deep neural learning or deep neural networking, deep learning techniques allow computers to learn through observation, imitating the way humans gain knowledge. 

The human brain contains many interconnected neurons, which act as information messengers when the brain is processing information (or data). These neurons use electrical impulses and chemical signals to communicate with one another and transmit information between different areas of the brain. 

Artificial neural networks (ANNs)–the underlying architecture behind deep learning–are based on this biological phenomenon but formed by artificial neurons that are made from software modules called nodes. These nodes use mathematical calculations (instead of chemical signals as in the brain) to communicate and transmit information within the model.