  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.
