A Proposed Large Language Model-Based Smart Search for Archive System
This addresses the problem of inefficient search in digital archives for archivists and users, though it appears incremental as it builds on existing RAG and LLM techniques.
The study tackled the problem of information retrieval in digital archival systems by developing a smart search framework using Large Language Models (LLMs) with a Retrieval-Augmented Generation (RAG) approach, resulting in significant improvements in search precision and relevance over conventional methods.
This study presents a novel framework for smart search in digital archival systems, leveraging the capabilities of Large Language Models (LLMs) to enhance information retrieval. By employing a Retrieval-Augmented Generation (RAG) approach, the framework enables the processing of natural language queries and transforming non-textual data into meaningful textual representations. The system integrates advanced metadata generation techniques, a hybrid retrieval mechanism, a router query engine, and robust response synthesis, the results proved search precision and relevance. We present the architecture and implementation of the system and evaluate its performance in four experiments concerning LLM efficiency, hybrid retrieval optimizations, multilingual query handling, and the impacts of individual components. Obtained results show significant improvements over conventional approaches and have demonstrated the potential of AI-powered systems to transform modern archival practices.