Retrieval Enhanced Transformer

Retrieval-enhanced transformers (RETs) improve large language models (LLMs) by augmenting their internal knowledge with information retrieved from external databases, addressing limitations in parameter size and computational cost. Current research focuses on optimizing retrieval methods (e.g., using various similarity metrics and efficient indexing techniques), integrating RETs with different LLM architectures (like GPT and others), and applying them to diverse tasks such as code completion, question answering, and click-through rate prediction. This approach offers a promising path towards building more powerful and efficient LLMs, particularly for knowledge-intensive applications, by leveraging vast external knowledge stores rather than solely relying on model parameters.