Retrieval Augmentation

Retrieval augmentation enhances large language models (LLMs) by incorporating external knowledge sources to improve accuracy, address hallucinations, and handle long contexts. Current research focuses on optimizing retrieval methods (e.g., k-NN, dense retrieval), integrating retrieved information effectively into LLMs (e.g., through modality fusion), and developing frameworks for managing and utilizing this external knowledge (e.g., dynamic retrieval based on model confidence). This approach is proving valuable across diverse applications, including question answering, text summarization, code generation, and even medical diagnosis, by improving factual accuracy and mitigating the limitations of LLMs trained solely on parametric knowledge.