Dense Passage Retrieval

Dense passage retrieval (DPR) aims to efficiently identify the most relevant text passages from large corpora to answer questions, serving as a crucial component in question answering systems. Current research focuses on improving DPR's speed and accuracy, exploring techniques like sentence selection, context structurization, and various pre-training methods (e.g., masked autoencoders, contrastive learning) to enhance embedding quality and retrieval effectiveness. These advancements are significant because efficient and accurate DPR is essential for building robust and scalable open-domain question answering systems and other information retrieval applications, particularly in domains with limited labeled data. Furthermore, research addresses challenges like hallucination in large language models and the robustness of DPR to adversarial attacks.