Citation generation aims to generate a citation sentence that refers to a chosen paper in the context of a manuscript. However, a rigid citation generation process is at odds with an author's desire to control specific attributes, such as 1) the citation intent, e.g., either introducing background information or comparing results, and 2) keywords that should appear in the citation text. To provide these degrees of controllability during citation generation, we propose to integrate the manuscript context, the context of the referenced paper, and the desired control attributes into a structured template and use it to fine-tune a language model (LM) via next-token prediction. We then utilize Proximal Policy Optimization to directly optimize the LM in favor of a high score of our proposed controllability metric. The proposed workflow harmoniously combines citation attribute suggestion and conditional citation generation into one LM, allowing for better user control.

翻译：引文生成旨在生成与手稿上下文中选定论文相关联的引文语句。然而，僵化的引文生成过程与作者对特定属性的控制需求相悖，例如：1）引文意图（如介绍背景信息或对比结果），2）引文文本中应出现的关键词。为在引文生成过程中实现这种可控性，我们提出将手稿上下文、引用论文上下文及所需控制属性整合为结构化模板，并通过下一标记预测对语言模型进行微调。随后利用近端策略优化直接优化语言模型，使其在我们提出的可控性指标上获得高分。该工作流将引文属性建议与条件式引文生成和谐地整合至同一语言模型中，从而增强用户对生成结果的控制能力。