Query Augmentation

Query augmentation enhances information retrieval and related tasks by improving the quality and diversity of search queries. Current research focuses on developing effective augmentation strategies, including leveraging relational databases, incorporating user preferences and brain signals, and employing diverse data augmentation techniques within various model architectures like BM25 and BERT-based dense retrievers. These advancements aim to improve the accuracy and robustness of systems across diverse applications, such as question answering, summarization, and few-shot learning, particularly in addressing challenges like hallucination and adversarial attacks. The ultimate goal is to create more reliable and efficient information access systems.