Relevance Judgment

Relevance judgment, the process of assessing the appropriateness of information to a given query, is crucial for improving search engine performance and various information retrieval tasks. Current research focuses on leveraging large language models (LLMs), including vision-language models and those fine-tuned on diverse datasets, to automate this process, often incorporating techniques like advanced prompting and multi-objective optimization to enhance accuracy and address biases. This automation is significant because it addresses the cost and scalability challenges of human-based relevance assessment, impacting the development and evaluation of search systems across various domains, from product search to legal case retrieval. The development of synthetic datasets and improved evaluation metrics are also active areas of investigation.