Controllable Citation Sentence Generation with Language Models
This addresses the need for more flexible citation generation for academic authors, though it is incremental as it builds on existing language model methods.
The paper tackles the problem of generating citation sentences with user-specified attributes like intent and keywords, achieving improved controllability by fine-tuning a language model with structured templates and optimizing it via Proximal Policy Optimization.
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.