Knowledge Adapter

Knowledge adapters are modular components designed to enhance the capabilities of large language models and other machine learning systems by incorporating external knowledge sources. Current research focuses on developing efficient methods for selecting and integrating these adapters, often employing techniques like finite state machines or structural embedding projections to manage knowledge from diverse and heterogeneous sources, including knowledge graphs and unstructured text. This approach addresses limitations in existing models, such as catastrophic forgetting and hallucination, leading to improved performance in tasks like knowledge graph completion, person re-identification, and image-text retrieval, with applications spanning various domains.