In-context learning primarily refers to the technique used by certain machine learning models, especially large language models, to dynamically interpret and respond to new data based solely on the context they are given during inference. This method leverages the patterns and information learned during the model's initial training phase and applies them to make predictions or generate responses that fit new, unseen prompts. The key aspect of in-context learning is that the model does not undergo further training or fine-tuning; instead, it infers answers based on a careful selection of examples provided in the prompt, effectively learning "on the fly." This ability is crucial for tasks requiring flexibility and adaptability in AI applications, particularly in scenarios where deploying a fully trained model isn't feasible.
Historical Overview: The concept of in-context learning has been particularly popularized with the advent of transformer-based models like GPT-3, introduced by OpenAI around 2020. The effectiveness of these models in leveraging contextual cues significantly highlighted the potential of in-context learning.
Key Contributors: OpenAI has been instrumental in advancing in-context learning through its development of models such as GPT-3. These models are designed to perform a wide range of tasks effectively merely by altering the input context, thus demonstrating the power of this approach.