Retrieval System

Retrieval systems aim to efficiently locate relevant information from vast datasets, a crucial task across diverse fields like information retrieval, legal analysis, and medical diagnosis. Current research emphasizes improving retrieval accuracy and efficiency through techniques like fine-tuning pre-trained embeddings, integrating large language models (LLMs) with retrieval-augmented generation (RAG), and developing novel architectures such as graph neural networks and vision transformers. These advancements are driving improvements in various applications, including question answering systems, personalized recommendations, and medical image analysis, ultimately enhancing access to and understanding of information.