RocketQA: Optimized Dense Passage Retrieval for Open-Domain Question Answering

RocketQA is an optimized dense passage retrieval framework designed to enhance open-domain question-answering systems. It uses a dual-encoder model architecture for retrieving relevant passages, where the query and document encoders are trained collaboratively to improve retrieval performance. The framework introduces innovative training techniques, such as cross-batch negatives, denoising hard negatives, and data augmentation, which address common challenges like sparse negative samples and noisy training data. By optimizing these methods, RocketQA ensures that its dense retrieval model is more effective at distinguishing relevant passages from irrelevant ones, even in scenarios where the negatives are contextually similar. This improvement directly enhances the precision and recall of the retrieval system, enabling better performance in open-domain QA tasks.