# Controllable Citation Sentence Generation with Language Models

**Authors:** Nianlong Gu, Richard H. R. Hahnloser  
**arXiv:** [2211.07066](https://arxiv.org/abs/2211.07066)  
**Date:** 2022-11-14  
**Summary page:** https://www.summarizepaper.com/en/paper/2211.07066v2/

## Key points

- Authors Nianlong Gu and Richard H. R. Hahnloser explore challenges of citation generation in academic writing
- They propose a structured template to fine-tune a language model for next-token prediction
- Utilize Proximal Policy Optimization to optimize the language model for high controllability scores
- The workflow combines attribute suggestion and conditional citation generation within a single framework
- Prioritizes user control and flexibility in generating citations
- Enhances citation practices in academic publications
- Promotes greater author autonomy and customization in scholarly writing

## Summary

In their paper titled "Controllable Citation Sentence Generation with Language Models," authors Nianlong Gu and Richard H. R. Hahnloser explore the challenges of citation generation in academic writing and propose a novel approach to address them. The traditional citation process often limits an author's control over important attributes such as citation intent and keyword inclusion. To overcome this limitation, the authors introduce a structured template that integrates various contexts and desired control attributes to fine-tune a language model (LM) for next-token prediction. They also utilize Proximal Policy Optimization to optimize the LM towards achieving high controllability scores. This innovative workflow combines attribute suggestion and conditional citation generation within a single LM framework, providing authors with enhanced control over citations. By prioritizing user control and flexibility in generating citations, this research contributes to advancing academic writing and scholarly communication. It not only streamlines the citation process but also empowers authors to tailor citations according to their specific needs and preferences. Overall, "Controllable Citation Sentence Generation with Language Models" offers valuable insights into enhancing citation practices in academic publications while promoting greater author autonomy and customization in scholarly writing.