Contextual Adapter

Contextual adapters are modules designed to enhance the adaptability of large language models and other machine learning systems by selectively incorporating external knowledge or context into their processing. Current research focuses on improving their efficiency, addressing data imbalance issues (especially in low-resource scenarios), and integrating them into various architectures, including transformers and neural transducers, for tasks like speech recognition, question answering, and robot control. This work is significant because it allows for improved performance on tasks involving rare words, domain adaptation, and personalized experiences, while mitigating the computational costs associated with full model retraining.