In-Context Learning
In-context learning is the ability of large language models to learn new tasks from examples provided within the input prompt, without any parameter updates. This emergent capability enables flexible task adaptation at inference time.
Understanding In-Context Learning
In-context learning is the ability of large language models to perform new tasks simply by being given examples or instructions within the input prompt, without any gradient updates or fine-tuning. This emergent capability, first prominently demonstrated by GPT-3, allows models to adapt their behavior based on a few demonstrations provided at inference time. Few-shot prompting, where several input-output examples precede the actual query, is the most common form of in-context learning. The mechanism behind this ability is still actively researched, with theories suggesting that transformer attention mechanisms implicitly implement learning algorithms. In-context learning has revolutionized how practitioners interact with language models, enabling rapid prototyping and task adaptation without the cost and complexity of fine-tuning or instruction tuning.
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.