Hybrid Retriever

Hybrid retrievers combine the strengths of different retrieval methods, such as sparse and dense techniques, to improve information retrieval accuracy and efficiency across diverse tasks, including question answering and entity linking. Current research focuses on optimizing training methods for these hybrid models, particularly in low-resource languages, and aligning their outputs with the preferences of downstream language models to enhance performance in retrieval-augmented generation. This work is significant because improved retrieval methods are crucial for advancing various applications, from conversational search and knowledge-intensive tasks to biological applications like protein function annotation.