Textual Database
Textual databases are being actively researched to improve how information is accessed and processed within large datasets of unstructured text. Current efforts focus on enhancing retrieval augmented generation (RAG) systems, employing techniques like deep language encoding and graph-hop retrieval to handle complex queries and multi-step reasoning across diverse data sources. These advancements aim to create more efficient and accurate question-answering systems, personalized models for specific applications (like weather forecasting), and improved methods for evaluating RAG performance without relying on human annotation. The ultimate goal is to bridge the gap between human language and machine understanding of large textual corpora, impacting fields ranging from scientific knowledge discovery to practical applications like receipt processing.