A Comprehensive Guide to Large Language Models (LLMs)

From ELIZA (1966) to GPT-3 (2022), the world has come a long way. Wondering what that means? Well, they symbolize the evolution of Large Language Models. Eliza was the first language model developed in 1966 which could give a human-like response to all the user queries. But due to a lack of proper infrastructure and not-so-advanced technology, that model was not that efficient and scalable.

Now we have access to highly advanced language models such as GPT-3, LaMDA, Megatron, etc. These language models can understand and generate any type of text to assist you in writing articles or writing codes for programming languages. But how do they work? What are their applications? And what future does this technology hold?

We are going to see everything in this article. We will also see how you can learn these technologies and advance your AI career. So, let’s get started.

What is a Large Language Model?

So, the first thing first – what is a large language model? As the name suggests, these are AI system models that are designed and developed to work on the language thing. And what are they capable of? They can generate texts, codes, translate languages, identify user queries, respond to them, and are perfect chatbots developed to make human life a bit easier.

Since a lot of data (lot means seriously a lot, like zeta bytes of data) is used to train these machine learning language models, they can efficiently identify patterns and relationships between different types of words and codes. This enables them to perform all kinds of NLP jobs.

Even though they are in the development phase, they are quite effective in answering user queries naturally like a human. Though they sound a bit mechanical, we can very much expect them to overcome such limitations in the coming future.

How do these Large Language Models work?

There are a lot of steps involved in building a Large Language Model. Here we try to break up these steps to understand how these LLMs work.

Step 1: Collection and preparation of data

A huge amount of data is required to train the LLMs. Data is gathered from different sources. Wikipedia and GitHub are some of the popular sources of information that are widely used to fetch textual data. These data are then cleaned for quality so that the machine-learning language model can identify the texts and the relationship between them in a clear and concise manner.

Step 2: Tokenization of data

This process refers to breaking down data into smaller forms. The token can be referred to as small words, phrases, codes, prefixes, suffixes, and all various kinds of linguistic components which can make identifying patterns between these entities clearer. These tokens might also be termed parameters. GPT-3 uses around 175 billion parameters to generate its output.

Step 3: Training and deployment

When the parameters have been identified and tokens have been generated, the language model is then trained on these huge data sets. They are fine-tuned to give clear outputs. Then the developers deploy the model, gauge their performance, monitor for any improvement and changes required and update it with the latest findings.

Types of Large Language Models

Developers use various types of transformer architecture to build their large language models. But it is not mandatory that a particular LLM should remain confined to one type of architecture only. Below are some of the popular LLM architectures:

• Autoregressive

• Autoencoding

• Encoder-decoder

• Bidirectional

• Fine-tuned

• Multimodal

GPT, BERT, LaMDA, PaLM, BLOOM, LLaMA, Claude, NeMO LLM, and Generate, are some of the most popular and widely used Large Language Models currently used across the globe.

How can you learn about these LLMS?

Students who want to make a successful AI career must be well equipped with the knowledge of LLM. Now many institutes provide full-time courses on Artificial intelligence and large language models. Students also have to choose from the best AI ML certifications to learn about these latest technologies.

An artificial intelligence certification covers topics related to transformer architecture, machine learning algorithms, Hyperparameter tuning, etc. that are very effective in learning and developing large language models. Some of the best AI ML certifications include:

- Certified Artificial Intelligence Consultant (CAIC™) from USAII®

- Graduate courses in AI from Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)

- AI courses from New York University

Conclusion

Large Language Models are a great technology that has been making developers’ and content creators' tasks easier. They are also assisting students and professionals from all walks of life in completing their assignments or assisting in their research work. Though LLMs are still in the development phase, they have gained a lot of popularity and a huge user base. In the coming years, we will see more advanced LLMs and expect them to be more accurate, more personalized, and less mechanical. Why not get prepared to learn about LLMs and give your AI career a push start?