# Personalized Scientific Figure Caption Generation: An Empirical Study on Author-Specific Writing Style Transfer

arXiv: https://arxiv.org/abs/2509.25817 · HTML: https://mlsanity.com/paper/2509.25817/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 4 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions](https://mlsanity.com/paper/2310.15405/) (2023)
- [Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization](https://mlsanity.com/paper/2302.12324/) (2023)

## Abstract (arXiv)

We study personalized figure caption generation using author profile data from scientific papers. Our experiments demonstrate that rich author profile data, combined with relevant metadata, can significantly improve the personalization performance of multimodal large language models. However, we also reveal a fundamental trade-off between matching author style and maintaining caption quality. Our findings offer valuable insights and future directions for developing practical caption automation systems that balance both objectives. This work was conducted as part of the 3rd SciCap challenge.