Fine Grained Retrieval

Fine-grained retrieval focuses on retrieving highly specific information or items from large datasets, going beyond simple keyword matching to capture nuanced similarities. Current research emphasizes improving retrieval accuracy and efficiency through various techniques, including the development of novel retrieval units (e.g., propositions instead of passages), attention mechanisms to focus on discriminative features, and the use of pre-trained models augmented with prompt tuning or contrastive learning. These advancements have significant implications for various applications, such as conversational question answering, image and video retrieval, and knowledge base question answering, by enabling more accurate and efficient information access.