Retrieval Augmented Thought

Retrieval Augmented Thought (RAT) enhances Large Language Models (LLMs) by integrating external knowledge retrieval into their reasoning process, aiming to improve accuracy, coherence, and the handling of complex tasks. Current research focuses on developing efficient retrieval methods and integrating them into various thought structures, such as tree-based approaches and sequential decision-making frameworks, to optimize the selection and use of retrieved information. This approach addresses limitations of LLMs in factual accuracy and long-context understanding, with demonstrated improvements in question answering, code generation, and other complex reasoning tasks, showing promise for applications requiring reliable and explainable AI.