What is LLM (Large Language Models)?

In 2026, artificial intelligence (AI) is an important part of the everyday lives of many people. Even if someone has not encountered terms and technologies like large language models (LLMs), generative AI, or machine learning, most individuals have used AI-powered tools in one way or another. Platforms like ChatGPT have played a vital role in the popularity of AI.

Behind all these AI tools are the technical components, technologies, and models that work together to make their functionality possible. This article explores those key elements, especially the different aspects of large language models (LLMs) to explain their key types and how they work.

What is a Large Language Model (LLM)?

A large language model, abbreviated as LLM, is a type of artificial intelligence. More specifically, it is a type of machine learning model trained on large datasets to recognize patterns and statistical relationships in natural language.

While a number of components are involved in an LLM, the most important thing to know is that it is trained on a vast amount of data. The dataset itself is collected from numerous sources, which enables the model to generate contextually appropriate responses in human-like language as per the user’s input. 

Origin of LLM in AI

While LLMs have certainly exploded in popularity in recent years, they’re something AI developers have been working on for a long time. The creation of LLMs has been possible due to decades of research and development in natural language processing, deep learning architecture, and machine learning algorithms. 

The use of statistical language models in the 1990s and the advancements of deep learning in the 2000s have all contributed to LLMs. 

The turning point in the development of large language models came in 2017 with the publication of the research paper called “Attention Is All You Need” by researchers at Google. 

The paper introduced a completely new deep learning architecture called the transformer, which has now become the foundation of modern LLMs, including Generative Pre-Trained Transformer (GPT) used by OpenAI.

Types of LLMs

As large language models have been developed for different purposes, they also vary in terms of scope, data, use cases, and training. These differences have led to several different types of LLMs meant to fulfill specific tasks or user requirements. The major types of LLMs are:

1. Task-Specific LLMs: These LLMs are developed and configured to complete specific tasks, such as text summarization, translation, or any other particular task. Grammarly’s AI features and OpenAI Whisper are two real-life examples of task-specific LLMs. 
2. Domain-Specific LLMs: The dataset is the foundation of any type of LLM. Domain-specific LLMs are trained on a dataset belonging to a specific niche, such as law, medicine, or finance. BloombergGPT, for example, is a finance-specific LLM.
3. Multilingual LLMs: With AI becoming popular all over the world and the need for communication with a global audience, LLMs trained to understand and generate text in multiple languages are called multilingual LLMs. 
4. General-Purpose LLMs: If you are wondering what is ChatGPT’s LLM is, the answer is that it is a general-purpose LLM. In fact, most of the popular AI platforms like Claude, Perplexity, and Gemini are also considered to be general-purpose LLMs as well as multilingual LLMs. 

Overall, there are no hard-and-fast rules when it comes to different categories and types of LLMs. Most of these models overlap in terms of their behaviour and use cases. For example, most of the LLMs today are multilingual in nature. Similarly, you can create projects within general-purpose LLMs and train them to perform specific tasks or work within a specific domain. 

What is an LLM and How Does it Work?

Now that we’ve discussed the basics of large language models and their key types, let’s understand in detail what exactly an LLM model is in terms of its working. 

A well-trained LLM model is required to create complex AI-powered systems capable of generating human-like text. However, an LLM model itself is composed of several different components that work together to achieve the level of linguistic expertise and accuracy required to meet the end user’s requirements. 

Machine Learning and Deep Learning

As stated before, machine learning is at the very foundation of LLMs. In simple words, machine learning is a part of AI that ensures a large amount of data can be used to train a program. More specifically, LLMs use a type of machine learning called deep learning. Deep learning models can train on large datasets to handle various problems with minimal human intervention.

LLM Neural Networks

The concept of neural networks is derived from the human brain. Just like the brain is made of neurons that send signals to each other, an AI neural network also has different network nodes that connect with each other. 

A large language model is built on neural networks that have several layers, including an input layer, hidden layers, and output layers. These are the generic names of the layers. Specific names depend on the specific neural network’s type.

LLM Transformer Models

Transformer models are the specific types of neural network architecture used in LLMs. These are neural networks that can model human language and also analyze the context, which is a core requirement of any LLM model.

Transformer models are better than the other types of machine learning because they are based on a mathematical technique that makes them better at detecting even minor connections between different elements, and hence, better at understanding context. 

This is also the reason why most AI tools like ChatGPT today can understand the input in human language, even if it is written in a vague manner.

3 Layers of Transformer Model

A transformer model has the following three layers that make it work:

1. Embedding Layer: This layer is the first step in processing an input. Its purpose is to convert words (called tokens) into numerical representations, so that the model can analyze them mathematically. 
2. Self-Attention Layer: This layer helps the model understand the contextual relationships between the tokens and also their meaning across entire paragraphs or sentences. 
3. Feedforward Network (FFN) Layer: This layer is part of the larger transformer block. It handles the non-linear transformation of data processed by this stage. The model uses this layer to make direct and complex relationships between the input and output. 

Previous language models like long short-term memory (LSTM) and gated recurrent units (GRUs) also used to have another layer called the recurrent layer to maintain memory. However, newer LLMs only rely on transformer models and self-attention and do not have the recurrent layer.

Importance of Large Language Models

Large language models have become important and popular across all types of businesses operating in various industries. AI has drastically lowered the barrier for even non-technical people to launch SaaS tools, businesses, and startups on a regular basis. 

Such widespread usage of LLM comes from the fact that they are capable of processing all sorts of queries and responding accordingly. Traditional programs or even chatbots typically had finite options because they used the common if/then programming statements. In comparison, LLMs can respond to natural human language and perform detailed data analysis to answer even unstructured questions or prompts. 

Applications of LLMs

Let’s better understand the importance of LLMs by looking at their applications across different industries, along with real life examples. 

Healthcare

LLMs are useful to summarize tons of patient data in healthcare facilities. It makes it easier for the doctors to document and also review a patient’s history quickly to make better medical decisions. For example, US hospitals are using Nuance DAX to transcribe doctor-patient conversations. 

Software Development

GitHub Copilot, powered by LLMs, is the most popular application of LLMs in technology and software development. It helps developers write code faster and also debug the code by getting AI-powered suggestions.

Customer Service

A large number of businesses, large as well as small ones, now use LLM-powered chatbots to improve customer service. Airlines, banks, apparel brands, and SaaS companies like Zendesk AI are all examples in this niche. 

Media and News

LLMs have streamlined the content creation process not only for solo content creators but also for large media companies and news organizations. Associated Press, for example, has been using AI and LLM for several years now to automate data-driven journalism. 

Finance

Finance is another major sector where LLMs can have a major impact on people’s lives. LLMs can analyze large finance-related datasets and help people make better financial decisions. BloombergGPT is a major tool in the industry that helps analysts process and understand financial data using natural language. 

Content Creation and Digital Marketing

A number of AI tools have emerged to facilitate or even automate different types of content creation, including generating videos (Synthesia), images (Midjourney), SEO-optimized blog posts (Jasper AI), social media posts (ContentStudio), general-purpose AI tools (ChatGPT), and many others. The use of LLMs in marketing and digital marketing affects every other industry, which further proves that LLMs are important for every sector.

How are LLMs Trained?

Let’s see how LLMs are trained to be able to understand complex queries in natural language and respond with maximum accuracy and efficiency.

What is LLM Training?

LLM training is the process of teaching a large language model to model and generate human language. It is possible by teaching the model numerous patterns from a massive dataset. The model then becomes capable of predicting the next token in an accurate sequence. 

What is Pretraining in LLM?

As the name suggests, pretraining is the foundational step of training an LLM. This involves training the model to understand basic language grammar, syntax, basic reasoning patterns, and world knowledge. A large amount of diverse datasets composed of books, code, and web pages is used in the pretraining phase. 

What is Post-Training in LLM?

The post-training stage in the process of training an LLM involves refining the pretrained model. This stage is necessary to improve the model’s usability, safety, and overall performance. Deep learning techniques like fine-tuning and reinforcement learning from human feedback (RLHF) are used to post-train a large language model. 

By the end of the post-training phase, an LLM is refined and optimized to respond to natural language as per the user’s expectations, while following the ethical guidelines. 

Role of Web Scraping in Data Collection and Training

A lot has been discussed throughout this article about how diverse datasets are the foundation of any well-developed large language models. The datasets made of web pages, forums, journal articles, and code repositories are possible because of web scraping. 

Web scraping can be defined as an automated process of collecting large amounts of publicly available data from websites. It involves using software tools capable of extracting text and information from the web at scale.

Therefore, web scraping is a critical part of collecting a massive amount of data quickly and training an LLM.

Conclusion

Large language models started out as a way of making computer systems more accessible by giving them the ability to process human language. But LLMs have also evolved over time and become the bedrock of generative AI. They have truly transformed the way machines interact with human language. With the help of training on large datasets, LLMs can generate responses in human-like language that correspond to users’ input. The developments in deep learning and natural language processing have supported the growth of LLMs, with the transformer architecture introduction in 2017 being a major milestone in their evolution. 

Key takeaways

• A large language model (LLM) is a powerful machine learning and deep learning model that can model and generate human language. 
• “Attention is All You Need”, a research paper published by Google scientists in 2017, formed the foundation of modern LLMs. 
• There are different types of LLMs, with general-purpose LLMs being the most popular type used by platforms like OpenAI’s ChatGPT and Anthropic’s Claude. 
• Deep learning techniques and neural networks, like transformer models, work together to make up a large language model.
• LLM models have applications across all industries and processes, such as content creation, customer support, text summarization, etc. 
• Web scraping is an essential technique to collect a large volume of diverse datasets and properly train an LLM. 

Other than the technical aspects, large language models have become critical to generative AI applications across diverse industries and use cases, ranging from customer service to specialized fields like finance and healthcare. With more research and investment in LLMs, the role of these models is likely to increase even more in AI-driven solutions and overall human-computer interaction.

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