Ce qui se cache derrière le fonctionnement de ChatGPT

ScienceEtonnante・2 minutes read

ChatGPT is a sophisticated chatbot using machine learning but faces challenges in creating effective conversations due to data limitations. The model's responses are based on training data, lacking the ability to recall past information or source citations accurately.

Insights

  • ChatGPT, a chatbot based on GPT models, uses self-supervised learning to generate responses, predicting the next word in text to create plausible continuations without a universal truth, allowing for billions of training examples without human annotations.
  • GPT models like GPT3 have vast parameters and context window sizes, with GPT4 potentially having even more extensive capabilities, recalling information from thousands of words earlier, but lacking memory of past conversations and providing responses based on associations rather than truth, highlighting the need for preprompts to guide responses and the challenge of accuracy in citing sources.

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Recent questions

  • What is ChatGPT?

    A chatbot designed for communication and assistance.

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Summary

00:00

Developing Chatbots with Self-Supervised Learning Models

  • ChatGPT is a chatbot or conversational agent designed to communicate with users to provide information or assistance.
  • Chatbots traditionally operate on keyword detection, but recent advancements have allowed for the development of more sophisticated models using machine learning.
  • Supervised learning, a common approach in machine learning, involves training models with large databases of examples to improve performance.
  • Creating an effective chatbot using supervised learning is challenging due to the need for vast amounts of high-quality data and the difficulty in ensuring continuity in conversations.
  • To overcome the limitations of supervised learning, a foundation model like GPT (Generative Pretrained Transformer) is used as a basis for more specific tasks.
  • GPT is trained to predict the next word in a text, creating plausible continuations based on existing data without a universal notion of truth.
  • GPT models like GPT3 have a vocabulary of around 50,000 tokens and can be used to generate text in multiple languages.
  • Training GPT models involves self-supervised learning, where large amounts of text data are used to create examples for the model to practice predicting the next word.
  • The training corpus for GPT models includes text from sources like Common Crawl, books, and Wikipedia, with different weights assigned to each source.
  • Unlike traditional supervised learning, self-supervised learning allows for training models without the need for specific human annotations, enabling the generation of billions of training examples.

12:34

"GPT's Limited Memory and Impressive Parameters"

  • GPT's knowledge is limited to a certain date due to being frozen, resulting in no memory of past conversations.
  • The model's fixed nature means it doesn't retain recent events or previous discussions.
  • GPT3 boasts 175 billion parameters, with GPT4 potentially having 6 to 10 times more.
  • During training, GPT processes entire texts, not just single sentences, for continuity and memory.
  • The context window size for GPT3 was 2048 tokens, increasing to 4096 for version 3.5 and over 32000 for version 4.
  • GPT4 can recall information from 25,000 words earlier in a text, equivalent to around a hundred pages.
  • GPT provides a list of possible words with associated probabilities rather than a single answer.
  • GPT's responses are based on associations from its training corpus, not necessarily truth.
  • GPT's completion of sentences is reasonable and plausible based on its training data.
  • To enhance GPT's chatbot capabilities, a preprompt can be used to guide the type of responses expected.

24:47

ChatGPT: Intelligent but struggles with citations

  • ChatGPT demonstrates intelligence and creativity by connecting seemingly unrelated concepts, producing interesting responses to various questions.
  • However, ChatGPT struggles with citing sources accurately, often generating non-existent references when asked for scientific publications, due to its text generation process lacking reasoning or internet search capabilities. This issue may be challenging to resolve solely through language modeling but could potentially be addressed by integrating search mechanisms with the model.
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