Part 2 – Simplifying AI Demystifying ChatGPT: What does the GPT mean in ChatGPT.

In the rapidly evolving world of artificial intelligence (AI), understanding the core technologies that power breakthroughs is crucial. One such technology is the Large Language Model (LLM) like ChatGPT. In this article, we will embark on a journey to demystify the inner workings of ChatGPT. We will explore the concept of transformers, delve into the roles of encoders and decoders, and discover why ChatGPT relies exclusively on the decoder model.

In the research paper, Generative pre-training is described as — the ability to train language models with unlabeled data and achieve accurate prediction.

Transformative Power of Transformers

At the heart of ChatGPT lies the revolutionary technology known as transformers. Transformers have fundamentally reshaped natural language processing (NLP) and have become a cornerstone of modern AI. But what exactly are transformers?

Transformers are a type of neural network architecture introduced in a 2017 paper titled "Attention Is All You Need" by Vaswani et al. Unlike their predecessors, transformers leverage a mechanism called self-attention. This mechanism allows the model to consider all words or tokens in a sequence simultaneously, enabling it to capture intricate relationships between them.

NLP Architecture Overview:

NLP, short for Natural Language Processing, is all about teaching computers to understand and work with human language. It's like teaching a computer to understand and speak in the same way we do. When it comes to NLP architecture, there are typically two important components: encoders and decoders.

1. Encoders: These are like the "understanders" of the NLP world. Their job is to take in human language, like the words and sentences we use, and convert it into a form that computers can work with. They break down sentences into numbers (vectors) that represent the words and their relationships.Vectors are nothing but a numerical representations of each word. Think of it as translating language into a language that computers can understand.

2. Decoders: Decoders are like the "generators." They take those numerical representations created by the encoders and use them to generate human-like text mainly where they are predicting the next word . It's as if they're translating computer language back into human language, but in a way that makes sense and is coherent.

Encoder-decoder models (also called sequence-to-sequence models) use both parts of the Transformer architecture. At each stage, the attention layers of the encoder can access all the words in the initial sentence, whereas the attention layers of the decoder can only access the words positioned before a given word in the input

?

Now, let's talk about ChatGPT:

ChatGPT's Approach:

ChatGPT is a bit different from the usual NLP models because it mainly relies on decoders without using traditional encoders. Decoder models use only the decoder of a Transformer model. At each stage, for a given word the attention layers can only access the words positioned before it in the sentence. These models are often called auto-regressive models. In simple words these are the models which are already pre-trained. ChatGPT ( Generative -Pre-Trained Models

Here's how it pulls it off:

The BASIC training process of GPT models consists of self-supervised learning mechanism. In simple words, a lot of text is gathered — strip the last word from the gathered text and then feed it as input to the transformer model, now check whether the output prediction matches the word that is stripped earlier — then backpropagate the error

Basically it is here that is gains the power of predicting the output :

for example : If I write , I , it would predict the next word as 'am' or 'have' or another verb like 'want'

of if i say "I would like to have a cup of ......" it will predict ' coffee' or 'tea' but not wine as it is glass of wine and not a cup of wine in English language.

In this second stage of training —

1. The model is given a prompt and generates different answers.
2. These answers are ranked by the human (rating it from best to worst).
3. These scores are then backpropagated.

Other than language generation, Transformer models can also be used for tasks like sentiment analysis.

How Decoders Complete the Task:

ChatGPT's decoders complete the entire NLP task because they are so versatile. Here's how they manage it:

• Generating Text: Decoders are fantastic at creating text that sounds like it's coming from a human. They use what they've learned during training to produce coherent and context-aware responses.
• Contextual Understanding: Decoders don't just respond blindly. They understand the context of the conversation, which helps them provide relevant and sensible answers.
• Remembering Past Interactions: By remembering what was said earlier in the chat, decoders can keep track of the conversation and provide accurate responses that fit with the ongoing discussion.

In essence, ChatGPT's decoders are like all-in-one language magicians. They both understand and generate text without needing a separate "translator" (encoder). This makes ChatGPT a powerful tool for conversation, text generation, and various NLP tasks, and it's why it can complete the entire language processing task on its own

.

Conclusion

In our exploration of ChatGPT, we've uncovered the transformative power of transformers, the roles of encoders and decoders, and the unique choice of ChatGPT to rely solely on decoders. This understanding gives us a glimpse into the inner workings of this remarkable AI model, which is changing the landscape of conversational AI and natural language generation.Some terms are definitely Machine Learning and AI specific, however I hope this article explains the basics.

As AI continues to advance, it's crucial to grasp the underlying technologies driving these innovations. ChatGPT, with its decoder-centric approach, stands as a testament to the adaptability and versatility of transformer-based models in shaping the future of AI-driven conversations and text generation.

References : https://gwern.net/doc/www/s3-us-west-2.amazonaws.com/d73fdc5ffa8627bce44dcda2fc012da638ffb158.pdf

#AI #ChatGPT #Transformers #NLP #ArtificialIntelligence

Follow me for more such articles at : https://tinyurl.com/nehakhasAI

?