论文标题
语言模型的可控引文句子产生
Controllable Citation Sentence Generation with Language Models
论文作者
论文摘要
引文产生旨在产生引用句子，该引文句子是指在手稿的背景下选择的论文。但是，严格的引文生成过程与作者控制特定属性的愿望不一致，例如1）引用意图，例如，引入背景信息或比较结果，以及2）应该出现在引文文本中的关键字。为了在引文生成期间提供这些可控程度，我们建议将手稿上下文，引用纸张的上下文以及所需的控制属性整合到结构化模板中，并通过下一步的预测将其用于微调语言模型（LM）。然后，我们利用近端策略优化来直接优化LM，而有利于我们提出的可控性指标的高分。提出的工作流程将引文属性建议和有条件引文生成结合到一个LM中，从而可以更好地控制用户。
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.
