Better Retrieval

Better retrieval aims to improve the accuracy and efficiency of information retrieval in various machine learning applications, particularly question answering systems. Current research focuses on enhancing retrieval methods within retrieval-augmented generation (RAG) frameworks, exploring techniques like leveraging inter-chunk interactions within documents, pre-computing query databases for faster matching, and employing semantic search and hybrid query strategies. These advancements are crucial for improving the reliability and performance of AI systems across diverse domains, from healthcare chatbots to open-domain question answering, by ensuring that models access the most relevant information for accurate and efficient responses.