Relevance Score

Relevance scoring aims to quantify the relationship between a query (e.g., a search term or question) and a piece of information (e.g., a document, image, or sentence), crucial for tasks like information retrieval and question answering. Current research emphasizes improving relevance scores through advanced techniques like transformer-based models, contrastive learning, and multi-modal approaches, often incorporating user feedback or logical reasoning to refine rankings. These advancements are driving improvements in various applications, including search engines, question-answering systems, and clinical trial matching, by enabling more accurate and efficient retrieval of relevant information.