The increasing demand for the deployment of LLMs in information-seeking
scenarios has spurred efforts in creating verifiable systems, which generate
responses to queries along with supporting evidence. In this paper, we explore
the attribution capabilities of plan-based models which have been recently
shown to improve the faithfulness, grounding, and controllability of generated
text. We conceptualize plans as a sequence of questions which serve as
blueprints of the generated content and its organization. We propose two
attribution models that utilize different variants of blueprints, an
abstractive model where questions are generated from scratch, and an extractive
model where questions are copied from the input. Experiments on long-form
question-answering show that planning consistently improves attribution
quality. Moreover, the citations generated by blueprint models are more
accurate compared to those obtained from LLM-based pipelines lacking a planning
component.

我们在这篇论文中探讨了最近显示对于生成的文本的逼真性、基础性和可控性有所改进的计划型模型的归因能力，该模型将计划概念化为一系列问题，旨在作为生成内容及其组织的蓝图。我们提出了两个利用不同蓝图变体的归因模型，一个是从头开始生成问题的抽象模型，另一个是从输入中复制问题的抽取模型。在长文问题回答的实验中，规划始终能够提高归因质量，此外，蓝图模型生成的引用文献与缺乏规划组件的基于 LLM 的流程获得的引用相比更加准确。