Scale Knowledge

Scale knowledge research focuses on how to effectively incorporate vast amounts of information into artificial intelligence models, aiming to improve their performance on tasks requiring rich world knowledge. Current efforts concentrate on developing efficient methods for transmitting and utilizing this knowledge, including techniques like federated distillation with accumulated local updates and retrieval-augmented language models that selectively access external knowledge bases like web-scale corpora. This research is crucial for advancing AI capabilities, particularly in natural language processing, by enabling models to handle complex, open-ended tasks and generalize better to real-world scenarios.