What is an LLM? A Look at the Large Language Model and Its Role in AI and Data Analytics

Have you ever thought about how AI chatbots like ChatGPT answer questions so naturally? The conversation can feel so human that it’s like talking to a friend. The technology behind them is the large language model (LLM), a form of generative AI that operates in a way that’s surprisingly similar to the human brain.

In this article, we’ll explain what a large language model is and examine its importance for both individuals and businesses in an era where data has become a valuable asset.

What is an LLM?

A large language model (LLM) is an artificial intelligence program trained to understand human language on a deep level. Think of it as a brilliant student who has read an immense library of books. Through this extensive reading, he or she learns to recognize linguistic patterns and sentence structures independently. Eventually, this person can apply this knowledge to generate new sentences, analyze information, and perform a variety of complex tasks.

An LLM not only creates text and translates languages with natural fluency but also becomes better in each new version. The goal is to interact with users as effectively as a knowledgeable colleague. Common examples that help clarify what an LLM is include ChatGPT, Bard, and Gemini. Such models can answer questions and produce text in many formats. Imagine having a personal AI consultant available to you anywhere, at all times.

At its foundation, the LLM is a branch of AI focused on natural language processing (NLP). It's designed to comprehend human language and then communicate in a way that sounds like the phrases and sentences people use in everyday conversation.

The market reflects this growing importance. Recent data from GII, a global research firm, projects the North American LLM market will reach $105.545 billion by 2030, showing a compound annual growth rate (CAGR) of 72.17%.

How many types of LLMs are there?

An LLM can be classified in several ways, with each type having different strengths and applications depending on the classification criteria. Here is a breakdown to give you a clearer picture.

1. Classification by architecture

• Transformers are the most popular architecture today. They are highly effective at generating words and sentences that closely resemble human communication. GPT and BERT are well-known examples of LLMs built on this architecture.
• An RNN (recurrent neural network) is an architecture designed for processing sequential data. However, it is limited in the length of data sequences it can effectively manage.
• A CNN (convolutional neural network) is an architecture inspired by the human brain's structure. While mainly used for image processing, it can also be adapted for language-related applications.

2. Classification by size

• Small models are LLMs with fewer parameters. They operate faster and require fewer resources, but their capabilities are more limited and less versatile.
• Medium models provide a good balance between size and performance. That quality makes them suitable for general-purpose tasks that are not excessively complex.
• Large models are the biggest LLMs, containing a huge number of parameters. They can handle complex instructions with high efficiency but demand substantial computational resources.

Popular examples of the large language model today

Now that you understand the different categories, let's look at a few examples of the LLM being widely used today.

1. GPT, a leader in the Generative AI era

Developed by OpenAI, GPT is recognized for its ability to generate natural-sounding text and accurately interpret user commands. Access to this LLM is typically available through applications or APIs.

2. BERT, an AI focused on understanding language

BERT AI was developed by Google and launched in 2018. Its primary function is to better comprehend human language, a field known as natural language processing (NLP).

3. LLaMA 3.1, a large language model with Thai support

Meta developed LLaMA 3.1 to stay competitive in the LLM field. It is capable of processing complex text and languages and can handle inputs up to 128,000 tokens long. Its support for the Thai language makes it useful for a range of applications, from translation, summarization, to content creation.

4. BLOOM, the largest open-access model

Currently, BLOOM is the world's largest open-access LLM, containing 176 billion parameters. It was trained on the NVIDIA AI platform to process text in 46 languages and can handle many text-related functions, including generation, summarization, classification, and translation.

Typhoon, a Thai LLM for the Thai language

As AI and data become global trends, local innovation is also taking place. SCB 10X, a Thai technology company, developed “Typhoon,” an LLM created specifically for the Thai language. It performs on par with GPT-3.5 in Thai. The project was born from the observation that while many English LLMs exist, very few are adapted for Thai, a challenge often caused by limited language data.

The Thai-developed LLM was created in two versions.

1. Its pretrained model involves teaching the model the Thai language, including vocabulary, cultural contexts, and general global knowledge.
2. Its instruction-tuned model can be trained further, improving its communication skills through user commands for tasks like summarization, translation, and Q&A.

Typhoon has gained notice from its performance on Thai language exams, where it has outperformed all other publicly available Thai models. Its performance is comparable to GPT-3.5 in Thai, making it a noteworthy LLM for the region.

Using LLMs and Data Analytics to Gain a Business Advantage

The LLM model and data analytics have become necessary tools for businesses aiming to get ahead in today's data-centric digital world. If the connection isn't immediately clear to you, here’s how they work together.

Analyze deep insights for multiple uses

The insights that can be gained from these LLMs have several advantages.

Gain a better understanding of customers

An LLM can analyze data from customer conversations, reviews, and feedback to provide a detailed picture of their needs, preferences, and behaviors. Such information helps businesses refine their products and services.

Forecast market trends

Data analytics helps in analyzing quantitative data, such as sales figures and purchase histories, to predict future market trends and consumer behavior. Doing so allows businesses to build effective strategies based on solid data.

Discover new opportunities

Combining an LLM with data analytics can help businesses identify opportunities they might have otherwise missed. For example, it could mean developing new products, entering new markets, or improving existing operational processes.

Improve operational efficiency

LLMs can also be used to increase efficiency in numerous ways.

Automate repetitive work

You can apply an LLM to answer common customer questions or to generate routine reports. Automation frees up your team to concentrate on higher-value work that contributes more to the business.

Support better decision-making

The insights gained from an LLM and data analytics allow business leaders to make faster, more informed decisions. This is a major advantage in a competitive and fast-changing market.

Reduce operational costs

Using an LLM and data analytics can help lower costs in areas like marketing, customer service, and the need for long-term external consultants.

Business examples of combined LLM and Data Analytics use

These advantages have advantages across virtually every business sector.

Retail and wholesale

Businesses with large inventories can use an LLM with data analytics to analyze customer purchasing habits. This analysis helps them recommend relevant products and design promotions that appeal directly to their target audience.

Finance and banking

In an industry where security is a top priority, financial institutions can use an LLM to analyze data to detect fraudulent activity, assess risk, and offer personalized financial advice to customers.

Healthcare

Organizations in the health sector, from hospitals to government agencies, can use these tools to analyze medical data, improve patient care, and support the development of new drugs and treatments.

Analyze Your Data to Find New Opportunities with Data Analytics

By now, you likely have a clearer answer to the question "what is an LLM?" and understand its importance. Its applications extend beyond general use and into the business world, where companies are using LLMs to save time and improve their operations.

When you pair this technology with data analytics, you can analyze all your business information and uncover details you might have overlooked. This process reveals both problems and opportunities, allowing you to create products, services, and marketing strategies that are well-suited to your target customers.