Explained: The Key Differences Between GPT-3 and ChatGPT – Which AI Language Model Reigns Supreme?

You have heard of ChatGPT and you might have heard of GPT-3 by Openai.com the company that started the hyper around AI back in November 2022. ChatGPT is a web-based interface where you can have a conversational dialogue with the AI whilst GPT-3 is accessible through an API (Application Programmable Interface).

Language Models:

So, you're probably familiar with how computers can “understand” language, right? Like, you can talk to Siri or Alexa and they can do things for you.

Well, GPT is a more advanced version of that. It's a type of computer program called a “language model,” and it's really good at understanding what people are saying or writing, and then responding in a way that makes sense.

However, GPT isn't perfect. Sometimes its responses might not be completely accurate, or it might miss something important that you said.

One reason for this is that GPT needs a starting point, called a “seed input,” to get going. This could be the first thing you type or say. Depending on what that seed input is, GPT might come up with a different response.

So, even though GPT is really advanced, it still has limitations and needs some human input to work well. But overall, it's pretty impressive that a machine can understand language and respond in a way that makes sense! The buttom line is YOU need to check facts and references

ChatGPT vs. GPT-3

GPT-3 and ChatGPT are both large language models that utilize artificial intelligence and natural language processing to understand and generate human-like text. However, there are some key differences between the two models.

  1. Purpose: GPT-3 is designed to be a general-purpose language model that can perform a wide range of natural language processing tasks, such as language translation, summarization, and question-answering. ChatGPT, on the other hand, is specifically designed to be a conversational agent, allowing it to engage in human-like dialogue with users.
  2. Size: GPT-3 is a much larger model than ChatGPT, with 175 billion parameters compared to ChatGPT's 6 billion parameters. This means that GPT-3 has a greater capacity for learning and generating text, making it capable of producing more accurate and diverse responses.
  3. Training data: GPT-3 is trained on a much larger and diverse dataset than ChatGPT, which allows it to better understand a wide range of topics and language patterns. ChatGPT, on the other hand, is trained on a smaller dataset that is specifically focused on conversational language.
  4. Applications: Because of its general-purpose design, GPT-3 has a wider range of potential applications, such as content creation, language translation, and text summarization. ChatGPT, on the other hand, is specifically designed for conversational applications, such as customer support, chatbots, and virtual assistants.
  5. Accuracy: While both models are designed to generate human-like text, GPT-3 is generally considered to be more accurate and diverse in its responses due to its larger size and more extensive training data.

Overall, while both GPT-3 and ChatGPT are powerful language models with similar capabilities, they are designed for different purposes and have different strengths and weaknesses.

ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response.

ChatGPT is fine-tuned from a model in the GPT-3.5 series, which finished training in early 2022. You can learn more about the 3.5 series here. ChatGPT and GPT-3.5 were trained on an Azure AI supercomputing infrastructure.

Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans


In summary:

ChatGPT is built upon a subset of GPT-3.5 and is a conversational AI. GPT is the base and is a much larger model. You will have to fact-check any output. The responses are based on word predictions and do not work like a search engine.

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