Retriever Generator

Retriever-generator models are increasingly used to improve information retrieval and generation tasks, particularly in complex domains like financial analysis and multilingual question answering. These models typically consist of a retriever component that selects relevant information from a knowledge base and a generator that uses this information to produce a desired output, such as a question, answer, or reasoning program. Current research focuses on improving the efficiency and accuracy of both components, often through techniques like iterative training, dual-feedback mechanisms, and the incorporation of large language models for enhanced semantic understanding. This approach holds significant promise for advancing natural language processing applications requiring access to and reasoning over large knowledge bases.