Towards In-Parameter Memory Augmentation for Large Language Models

In-parameter memory augmentation provides a way to encode post-training knowledge, such as user preferences or domain facts, directly into model parameters or adapters. This approach avoids the context window limitations and repeated encoding costs associated with in-context learning by integrating reusable information into the inference forward pass.