LLMs in Generative Artificial Intelligence

CHATGPT AND BARD are the talk of the town, these days. Both are conversational Artificial Intelligence (AI) bots that can respond to and communicate with humans to provide comprehensive information in response to queries.

- Jayesh Shah

They can be a virtual assistant and accomplish tasks like organising meetings and replying to messages and emails. They can also create content like blogs and scripts, generate and debug code, and perform the role of a sophisticated search engine with intelligent responses. The question is, what is under the hood of these fascinating tools? It is the LLMs or Large Language Models.

HOW DO LLMS WORK?

The LLMs are designed to understand natural language. These models are trained on extremely large datasets that have millions of parameters, in some cases, even billions. Simply put, an LLM consists of text data that tools like ChatGPT and Bard can parse through to generate a response to the query input by a user. This data comes from a variety of sources like books, magazine articles, Wikipedia, social media, etc. The more the parameters, the deeper the knowledge of the model and better the responses one can get from it.

The LLMs use transformer neural networks, a type of deep learning algorithm. In fact, GPT in ChatGPT stands for generative pre-trained transformer. The transformer algorithm analyses data sequentially, spots patterns in the phrases to understand context and predicts the next word based on the context as it comprehends it. It then goes on to predict the next sequence of words till the response is complete. Thus, LLMs can also be called next word prediction engines.

HOW ARE LLMS TRAINED?

The LLM training is spread over three phases.

In the pre-training stage, the model imbibes grammatical rules of the language from the large volume of text data that has been obtained from various sources. It understands the meaning of individual words and the arrangement of those words to build sentences. During this period, it also learns about word patterns.