Relevance Label

Relevance labeling, the process of assigning scores or labels indicating the importance or usefulness of information (e.g., search results, retrieved documents) to a given query, is crucial for optimizing information retrieval systems. Current research focuses on automating this process using large language models (LLMs) and reinforcement learning, often incorporating techniques like contrastive learning and multi-objective optimization to improve accuracy and efficiency. These advancements are significant because high-quality relevance labels are essential for training effective retrieval models and improving the performance of various applications, including search engines, question answering systems, and retrieval-augmented generation. The development of novel benchmark datasets and evaluation metrics further contributes to the field's progress.