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。

UniGen: 一个面向检索和问答的统一生成框架，利用大型语言模型

UniGen: A Unified Generative Framework for Retrieval and Question  Answering with Large Language Models

Query-focused summarization (QFS) is a challenging task in natural language
processing that generates summaries to address specific queries. The broader
field of Generative Information Retrieval (Gen-IR) aims to revolutionize
information extraction from vast document corpora through generative
approaches, encompassing Generative Document Retrieval (GDR) and Grounded
Answer Retrieval (GAR). This paper highlights the role of QFS in Grounded
Answer Generation (GAR), a key subdomain of Gen-IR that produces human-readable
answers in direct correspondence with queries, grounded in relevant documents.
In this study, we propose QontSum, a novel approach for QFS that leverages
contrastive learning to help the model attend to the most relevant regions of
the input document. We evaluate our approach on a couple of benchmark datasets
for QFS and demonstrate that it either outperforms existing state-of-the-art or
exhibits a comparable performance with considerably reduced computational cost
through enhancements in the fine-tuning stage, rather than relying on
large-scale pre-training experiments, which is the focus of current SOTA.
Moreover, we conducted a human study and identified improvements in the
relevance of generated summaries to the posed queries without compromising
fluency. We further conduct an error analysis study to understand our model's
limitations and propose avenues for future research.

该论文提出了一种利用对比学习来改进模型关注输入文档中最相关区域的新方法 QontSum，应用于基于查询的摘要生成，强调 QFS 在 Gen-IR 中 Grounded Answer Generation（GAR）上的作用，并通过基准测试数据集和人类评估说明了它的有效性和实用性。