Semi Parametric Language Model

Semi-parametric language models combine the strengths of traditional neural networks with external knowledge sources, aiming to improve accuracy, efficiency, and adaptability in natural language processing. Current research focuses on enhancing retrieval methods (e.g., nearest neighbor search, selective memorization), developing efficient architectures for integrating retrieved information (e.g., mixture-of-experts models), and applying these models to various tasks like question answering and continual learning. This approach offers a promising alternative to solely scaling up model size, potentially leading to more efficient and knowledgeable language models with improved performance on diverse downstream tasks.