In-Context Learning
The ability of large language models to learn new tasks from examples provided within the input prompt, without any parameter updates or traditional training.
In-context learning was first demonstrated at scale with GPT-3 in 2020 and has become a defining capability of large language models. The model adapts its behavior based on the examples and instructions in the prompt, effectively learning on the fly. This eliminates the need for task-specific fine-tuning in many cases, dramatically reducing the cost and time to deploy AI for new applications. Research suggests in-context learning emerges as a capability at sufficient model scale, typically above 10 billion parameters.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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