  Generative information retrieval, encompassing two major tasks of Generative
Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained
significant attention in the area of information retrieval and natural language
processing. Existing methods for GDR and GAR rely on separate retrieval and
reader modules, which hinder simultaneous optimization. To overcome this, we
present \textbf{UniGen}, a \textbf{Uni}fied \textbf{Gen}erative framework for
retrieval and question answering that integrates both tasks into a single
generative model leveraging the capabilities of large language models. UniGen
employs a shared encoder and two distinct decoders for generative retrieval and
question answering. To facilitate the learning of both tasks, we introduce
connectors, generated by large language models, to bridge the gaps between
query inputs and generation targets, as well as between document identifiers
and answers. Furthermore, we propose an iterative enhancement strategy that
leverages generated answers and retrieved documents to iteratively improve both
tasks. Through extensive experiments on the MS MARCO and NQ datasets, we
demonstrate the effectiveness of UniGen, showcasing its superior performance in
both the retrieval and the question answering tasks.
