Adaptive In-Context Learning 🤝 Vespa - part one

Large Language Models (LLMs) like GPT-4 are explored for their capability to perform In-Context Learning (ICL), where examples are added to the prompt instead of updating model parameters. This approach enables models to handle tasks like categorizing online banking support requests without retraining, allowing for flexibility and adaptability as new categories or labels emerge. Unlike traditional machine learning, ICL does not require dedicated infrastructure for training and serving models, simplifying the pipeline and democratizing machine learning. Vespa enhances this process by adaptively selecting context-sensitive examples during inference, treating it as an information retrieval problem to improve accuracy and manage large label spaces. Various retrieval techniques such as query performance prediction, neural ranking, and facets are employed to ensure diverse and relevant examples are used in prompts, thereby creating a data flywheel that continuously improves the model's performance.