How does ChatGPT work? Difference between GPT 3.5 and GPT 4.
Artificial Intelligence (AI) and Gen AI are revolutionizing the way we interact with technology, and at the forefront of this revolution is ChatGPT, a cutting-edge language processing model developed by OpenAI. In this blog, we’ll delve into the workings of ChatGPT and explore the key differences between its prominent versions, GPT-3.5 and GPT-4. Understanding these advancements is crucial as they not only showcase AI's rapid evolution but also potentially transform how we communicate, work, and innovate in the future.
Overview of ChatGPT:
ChatGPT, developed by OpenAI, is a state-of-the-art language processing model that uses deep learning to generate human-like text. It is designed to understand and respond to natural language inputs, making it ideal for a wide range of applications, from chatbots to content generation. In this blog, we’ll explore the inner workings of ChatGPT and compare its two major versions, GPT-3.5 and GPT-4, to understand the advancements made in AI-driven natural language processing.
The architecture of making a ChatGPT:
The above image gives the idea of the architect of ChatGPT.
To understand the image we need to know about LangChain, which is a module in Python used for making chatbots. Now the PDF is the data that we give for making the chatbot. Then the data is divided into chunks as smaller parts. As we earlier seen embeddings are the numeric form for making the system understand the input. Then the data is stored in vector store (faiss it is in Python). And this is how we can make our chatbot. This is a rough idea for the architect of ChatGPT.
We can also make a chatbot without having any knowledge of coding as we can get the code from ChatGPT and API also from OpenAI.
Essential Terminologies:
Large Language Models (LLMs)
Just to understand LLM in a very easy way, when we are on our smartphone trying to text somebody and there is a predictive text feature that comes in there i.e. system predicts the next word that prediction is LLM. But at a higher level, it is more complex and there is more accuracy and there are also other components involved. Similarly, when we type something on ChatGPT, and give us a response that is LLM. That’s why ChatGPT is also known as LLM. LLM only deals with text, it’s only for generating text.
It can be used in various fields like Text Generation, Summarization, Language Understanding, Chatbots, etc.
Prompt Engineering
This is a very growing field and there are a lot of opportunities coming for prompt engineers and it is the core concept of Gen AI. Let’s first learn about prompts, when we talk to Alexa, Siri, or Google and ask about anything like How’s the weather, this is a prompt. When we give any instruction or question to get the desired response that is a prompt. ChatGPT also remembers the prompt that we have feeded in it.
Embeddings
Embeddings are numerical representations of words, phrases, or sentences that capture semantic relationships between them. These representations are used in natural language processing tasks to help machine learning models understand the context and meaning of words in a computationally efficient way. Only LLM understands embeddings and uses them to generate responses.
Fine Tuning
Fine-tuning is a technique used in machine learning, particularly in the context of pre-trained models like ChatGPT. It involves taking a pre-trained model and further training it on a specific task or dataset to improve its performance on that task. Fine-tuning allows the model to adapt its learned representations to the nuances of the new task, often leading to better results compared to training a model from scratch. It uses Supervised and Reinforcement learning.
Evolution Unveiled: A Comparative Analysis of ChatGPT 3.5 and ChatGPT 4.0
ChatGPT, the advanced language model developed by OpenAI, has undergone significant improvements from its 3.5 to 4.0 versions. Understanding these changes is key to appreciating the strides made in natural language processing (NLP) technology. Let’s take a closer look at the differences between ChatGPT 3.5 and 4.0, and how these changes impact its functionality and performance.
One of the most noticeable differences between ChatGPT 3.5 and 4.0 is the size and complexity of the models. ChatGPT 4.0 is larger and more complex, with a greater number of parameters. This increase in size allows ChatGPT 4.0 to process and understand language more effectively, leading to improved performance in various NLP tasks.
With the increase in size and complexity comes a notable performance improvement. ChatGPT 4.0 exhibits better language understanding, generation, and contextual reasoning capabilities compared to ChatGPT 3.5. This means that ChatGPT 4.0 is better equipped to generate more coherent and contextually relevant responses to user queries.
Another significant difference between ChatGPT 3.5 and 4.0 is the training data used to train the models. ChatGPT 4.0 has been trained on a more extensive and diverse dataset, which includes a broader range of topics and contexts. This extensive training data allows ChatGPT 4.0 to better understand and generate text across a wide range of subjects.
ChatGPT 4.0 introduces several new features and functionalities that were not present in ChatGPT 3.5. These include improved fine-tuning capabilities, which allow developers to customize the model for specific tasks or industries more effectively. Additionally, ChatGPT 4.0 may offer enhanced support for multi-modal inputs, allowing it to process not just text but also other forms of data such as images or audio.
The advancements in ChatGPT 4.0 open up new possibilities for its use in various applications. For example, ChatGPT 4.0 could be used to develop more advanced chatbots that can provide more accurate and helpful responses to user queries. It could also be used to improve language translation services, making them more accurate and efficient.
As with any advanced AI technology, there are ethical considerations to take into account when using ChatGPT 4.0. For example, there may be concerns about bias in the training data or the potential for misuse of the technology. Developers and users of ChatGPT 4.0 need to be aware of these ethical considerations and take steps to mitigate them.
The advancements in ChatGPT 4.0 represent a significant step forward in NLP technology. Looking to the future, we can expect to see further improvements in AI-driven text generation and understanding. ChatGPT 4.0 sets the stage for more advanced language models that can provide even more accurate and contextually relevant responses to user queries.
In conclusion, the evolution from ChatGPT 3.5 to 4.0 marks a significant advancement in NLP technology. The improvements in performance, model size, training data, and new features make ChatGPT 4.0 a powerful tool for a wide range of NLP applications. As AI technology continues to evolve, we can expect to see even more impressive advancements in the field of NLP.
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