Generative Pre-trained Transformer
A Generative Pre-trained Transformer (GPT) is a type of large language model that generates text by predicting the next token in a sequence. Pre-trained on vast text corpora, GPT models exhibit broad language understanding and generation capabilities.
Understanding Generative Pre-trained Transformer
A Generative Pre-trained Transformer (GPT) is a type of large language model that uses the transformer architecture to generate coherent, contextually relevant text after being pre-trained on vast text corpora in a self-supervised manner. The "pre-trained" aspect means the model first learns general language patterns from billions of documents before being adapted through fine-tuning or prompt engineering for specific tasks like summarization, translation, question answering, and code generation. OpenAI's GPT series, from GPT-2 to GPT-4 and beyond, demonstrated that scaling model size, training data, and compute leads to emergent capabilities including few-shot learning and complex reasoning. GPTs use an autoregressive approach, predicting one token at a time based on all preceding tokens. These foundation models have catalyzed the generative AI revolution and underpin applications like ChatGPT, transforming how people interact with AI across industries.
Category
Generative AI
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Chain of Thought
Chain of thought is a prompting technique that encourages large language models to break down complex reasoning into intermediate steps. This approach significantly improves performance on math, logic, and multi-step reasoning tasks.
ChatGPT
ChatGPT is an AI chatbot developed by OpenAI that uses large language models to generate human-like conversational responses. It became one of the fastest-growing consumer applications in history after its launch in November 2022.
Claude
Claude is an AI assistant developed by Anthropic, designed to be helpful, harmless, and honest. It is built using Constitutional AI techniques and competes with models like GPT-4 and Gemini.
Diffusion Model
A diffusion model is a generative AI model that creates data by learning to reverse a gradual noise-adding process. Diffusion models power state-of-the-art image generation systems like Stable Diffusion and DALL-E.
Discriminator
A discriminator is the component of a GAN that learns to distinguish between real and generated data. It provides feedback to the generator, creating an adversarial training dynamic that improves output quality.
Few-Shot Prompting
Few-shot prompting provides a language model with a small number of input-output examples in the prompt to demonstrate the desired task format. This technique helps models understand task requirements without any fine-tuning.
Foundation Model
A foundation model is a large AI model trained on broad data that can be adapted to a wide range of downstream tasks. GPT-4, Claude, Gemini, and DALL-E are examples of foundation models that serve as bases for specialized applications.
GAN
A GAN (Generative Adversarial Network) is a generative model consisting of two competing neural networks — a generator and a discriminator. GANs produce realistic synthetic data by training these networks in an adversarial game.