Context Retrieval

Context retrieval focuses on enhancing large language models (LLMs) by providing them with relevant external information to improve accuracy and reliability, particularly in addressing factual inaccuracies and hallucinations. Current research emphasizes optimizing retrieval methods, including prompt engineering and the development of novel architectures like those incorporating semantic graphs or visual tokens to handle diverse data types and longer contexts. This work is significant because effective context retrieval is crucial for deploying LLMs in high-stakes applications like healthcare and scholarly question answering, while also improving efficiency and reducing reliance on massive model parameters.