Hello everyone and welcome back to the Cognixia podcast. Every week, we get together to talk about the latest happenings, bust some myths, discuss new concepts, and much more from the world of emerging digital technologies. From cloud computing to DevOps, containers to ChatGPT, and Project management to IT service management, we cover a little bit of everything weekly to inspire our listeners to learn something new, sharpen their skills, and move ahead in their careers.
In today’s episode, we talk about something that has been around for quite some time, it is the backbone of some very popular tools and platforms. We are talking about large language models. In this episode, we will discuss what language models are, what they do, how they are helpful, the potential they hold, and more, so keep listening.
To understand what large language models are, we first need to understand what transformer models are. As a human, we see the text as one word at a time and comprehend it accordingly, whereas machines see the text as just a bunch of characters. Machines were usually unable to interpret text like human beings can. However, this began changing when Vaswani et al published a paper establishing something called the transformer model. A transformer model is based on the attention mechanism, which enables the machine to read an entire sentence or even an entire paragraph instead of one character or one word at a time, and once the machine has consumed the entire input text, it will be capable of producing an output based on the input received. This enables the transformer model to understand the context of the input and deliver better outputs. These transformer models are the basis of many other models commonly used in machine learning and generative AI today. They process data by tokenizing the input and simultaneously conducting mathematical equations to
Large Language Models are more advanced and complex versions of the transformer model in a way. A large language model is a deep learning algorithm that can perform a variety of natural language processing tasks. The large language models use transformer models and are trained using very, very large data sets. This is also why the models are called LARGE language models. Due to the wide training and powerful transformer models at the backbone, the large language models are equipped to recognize, translate, predict, or generate text or other content. From understanding protein structures to writing code, these large language models can be trained to do a very wide range of things.
But how are transformer models and other machine learning models able to predict text? According to a very influential and interesting paper by Claude Shannon titled “Prediction and Entropy of Printed English”, the English language has an entropy of 2.1 bits per letter, despite having 27 letters, that is, 26 alphabets and 1 space, hence, 27. If these letters were used randomly, the entropy would be about 4.8 bits per letter, which would make it easier for machine learning models, especially the transformer models to predict what would come next in a human language text. The models keep repeating this process again and again, creating entire paragraphs, word by word, that we then receive as an output.
Also, how does the transformer model or the machine learning model comprehend and deal with grammar? The model sees grammar as a pattern of how different words are used in a sentence or a context. It would be challenging for anyone to list out all the rules of grammar and then teach them to a machine-learning model. Instead, the models are programmed to acquire these grammar rules implicitly using examples. When the transformer model is large enough, as is the case for large language models, the model can be trained to learn a lot more than just the grammar rules, learning to extend these ideas beyond just the examples it has been trained on.
We mentioned earlier that a lot of popular tools and platforms are built on the back of these large language models. So, which are some of these tools powered by large language models? The most popular tool powered by large language models is, of course, ChatGPT. It is powered by the LLM – GPT-3.5 and the premium version is powered by GPT-4. Another popular LLM is BERT by Google. Then there is one called Claude and it focuses on constitutional AI. There is an enterprise LLM called Cohere, which is not tied to one single cloud, unlike OpenAI tools. Baidu has another large language model called Ernie which powers the Ernie 4.0 chatbot. The Technology Innovation Institute has developed another transformer-based, casual decoder-only LLM called Falcon 40B. Meta has an LLM called Galactica trained in an expansive collection of academic material and another LLM called Llama. Google has another family of LLMs under Google Brain called Lamda. Microsoft has another LLM called Orca, and yet another called Phi-1. The BARD tool by Google is powered by PaLM.
These are just some examples of popular large language models built by different companies for different purposes. Many, many more large language models have been developed or are currently under development. It showcases how increasingly important large language models are becoming and how the world is increasingly relying on LLM-powered tools to accomplish various tasks. This should also give you a fair idea of how sought-after professionals trained in building and working with large language models are in the world today.
Keeping the potential and the growing demand in mind, Cognixia has launched a Working with Large Models training & certification in its renowned live online instructor-led format. The course is designed to offer an in-depth understanding and extensive hands-on experience working with a spectrum of Generative AI models, including Txt2Txt, Img2Img, and Multimodal models, as well as PaLM -2, spread over 40 hours of intensive online learning. To know more about this course, you can visit our website – www.cognixia.com and you can ask us any questions you might have about the course by connecting with us directly via the chat function on the website.
We are sure you would find the course very useful and a great addition to your resume.
With that, we come to the end of this week’s episode of the Cognixia podcast. We hope you found the episode today interesting and insightful, and feel inspired to learn something new with it. We will be back again next week with another exciting new episode of the Cognixia podcast. Until next week then.
Happy Learning!
