Autoregressive Search Engine

Autoregressive search engines represent a novel approach to information retrieval, aiming to improve the efficiency and relevance of search results by directly generating document identifiers or relevant text snippets using autoregressive language models. Current research focuses on adapting these models for various tasks, including smart reply systems and fact verification, exploring both end-to-end training methods and leveraging the capabilities of large language models for zero-shot or few-shot retrieval. This approach offers the potential for more efficient and accurate search, particularly in knowledge-intensive applications, by directly integrating retrieval into the language model's generation process, thereby eliminating the need for separate retrieval and ranking stages.