ChatGPT vs. Lex: AI, datasets, biases and uses
ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) language model that has been fine-tuned for chatbot applications. It is designed to generate natural language responses to input prompts, and it can do so in a wide variety of languages and domains.
The underlying architecture of ChatGPT is based on a transformer neural network, which is a type of machine learning model that is particularly well-suited for processing sequential data such as text. The model is pre-trained on a large dataset of text, which allows it to learn the patterns and structure of language.
When ChatGPT is given an input prompt, it uses its knowledge of language patterns to generate a response that is likely to be appropriate and coherent. The model does this by considering the context of the input and generating text that follows the rules of grammar and syntax.
To generate responses, ChatGPT uses a process called “attention,” which allows it to focus on specific parts of the input and use that information to generate a response. The model also has the ability to modify its responses based on feedback, allowing it to learn and improve over time.
Concretely, here’s how ChatGPT works:
- The model is trained on a large dataset of human conversation, such as chat logs or social…
