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.

UniGen 是一种集成了生成式检索和问答功能的统一生成模型，通过使用大型语言模型，UniGen 采用一个共享编码器和两个不同的解码器，引入连接器来弥补查询输入和生成目标、文档标识符和答案之间的差距，并提出了一种迭代增强策略来通过生成的答案和检索的文档迭代地改进两个任务，在 MS MARCO 和 NQ 数据集上的广泛实验证明了 UniGen 在检索和问答任务中具有优越的性能。