What are LLMs and GPT, Their Working & How they are Used in ChatGPT?

What Are Large Language Models?

Large Language Models are defined as deep learning models that use massive amounts of text data to learn the statistical properties of data. In LLMs, a neural network is pre-trained on a large corpus of the text data, such as web pages, books and articles. By the data provided, the model learns the structures and patterns in an unsupervised way. Once the data get trained, it can be fine-tuned to do specific tasks, such as sentiment analysis, or question-answering. 

The advantage of using LLMs is that they can understand nuances of language such as context, ambiguity and sarcasm, which is not quite apparent in traditional rule base systems. 

Apart from that, LLMs are also capable of producing human-like or natural-sounding text which opens up new possibilities for language-based applications. 

What is GPT and How it Works?

GPT is defined as a family of LLMS that was developed by AI. Since 2018, several versions have been released. The recent release is GPT-4 with 100 Trillion Parameters and it supports both text and image data. 

The architecture of GPT is dependent on the Transformer model. The model uses a self-attention mechanism to encode the input sequence into a queue of hidden states and then it is used to generate the output sequence. 

The GPT model is trained on a large amount of provided data through unsupervised learning. The pre-training of this model has two states. The first state is the masked language modelling (MLM) task, where the model is trained to predict a missing word for a sentence given. The second state is next sentence prediction, where the model is trained to predict if the two sentences are consecutive or not. 

How LLMs Are Used in ChatGPT?

ChatGPT is based on the GPT model and is a conversational AI system that can generate natural-sounding responses to the inputs users submit. LLMs are the backbone of ChatGPT. The main function of LLMs in ChatGPT is to encode user input, generate responses and decode them back into a natural language. Here is a little information on how the LLMs support ChatGPT: 

1. Encode User Input: Whenever any user enters a message, that message is encoded into a line of tokens through a tokenizer. After that, the tokenizer breaks the message down into individual works and converts them into numeric so that could be understood and processed by LLM. 
2. Generate Response: When the encoded message is fed into LLM, it uses the pre-trained knowledge to give a response. The LLM creates a probability distribution over the entire vocabulary of possible responses and selects the one that is the most likely response based on the message and the previous chat history. 
3. Decode Message: As the data is fed in numeric form, the results are also generated in a numeric form which is then converted into natural language by a decoder. 
4. Fined Tuning: ChatGPT can be fine-tuned for specified tasks and domains to improve the accuracy and relevance of results.
5. Personalized Experience: ChatGPT can be personalized based on previous interactions and user profiles. This aids ChatGPT in providing more ‘specific’ responses.

Conclusion

Summing it up, it can be said that GPT has revolutionised the field of NLP. Making opportunities for new apps and services that were unthinkable before their launch. The most critical factor in LLMs is to learn from massive data and then generate a response in human language. That is what is known until now. Let’s see where the future takes us.