Enriched Hard Query

Enriched hard query techniques aim to improve the performance of information retrieval and machine learning models on challenging, less-frequent queries. Current research focuses on developing methods to augment these difficult queries, for example, by using large language models to expand their scope or by employing specialized rankers trained on these enriched queries. These approaches, often incorporating techniques like dual queries or low-rank approximations, show significant improvements in various tasks, including text ranking, image retrieval, and in-context learning, demonstrating the value of tailored strategies for handling complex queries. The resulting advancements have substantial implications for improving the accuracy and efficiency of diverse applications relying on effective query processing.