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.

翻译：生成式信息检索涵盖生成式文档检索与有据答案生成两大核心任务，近年来在信息检索与自然语言处理领域备受关注。现有针对GDR和GAR的方法依赖相互独立的检索与阅读模块，难以实现联合优化。为此，我们提出\textbf{UniGen}——一个融合检索与问答的统一生成式框架，通过利用大语言模型的能力将两项任务集成至单一生成模型中。UniGen采用共享编码器与两个独立解码器分别实现生成式检索与问答功能。为促进双任务协同学习，我们引入由大语言模型生成的连接器，以弥合查询输入与生成目标之间、文档标识符与答案之间的语义鸿沟。此外，我们提出迭代增强策略，通过生成的答案与检索文档交替优化两项任务。在MS MARCO和NQ数据集上的大量实验表明，UniGen在检索与问答任务中均展现出卓越性能，验证了该框架的有效性。