# Leveraging Author-Specific Context for Scientific Figure Caption Generation: 3rd SciCap Challenge

arXiv: https://arxiv.org/abs/2510.07993 · HTML: https://mlsanity.com/paper/2510.07993/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 9 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://mlsanity.com/paper/2406.11695/) (2024)
- [SciCap: Generating Captions for Scientific Figures](https://mlsanity.com/paper/2110.11624/) (2021)
- [Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content](https://mlsanity.com/paper/2110.04406/) (2021)
- [Do Large Multimodal Models Solve Caption Generation for Scientific Figures? Lessons Learned from SciCap Challenge 2023](https://mlsanity.com/paper/2501.19353/) (2025)
- [Proposal Report for the 2nd SciCAP Competition 2024](https://mlsanity.com/paper/2407.01897/) (2024)
- [Human Variability vs. Machine Consistency: A Linguistic Analysis of Texts Generated by Humans and Large Language Models](https://mlsanity.com/paper/2412.03025/) (2024)
- [LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles](https://mlsanity.com/paper/2506.06561/) (2025)
- [Neural at ArchEHR-QA 2025: Agentic Prompt Optimization for Evidence-Grounded Clinical Question Answering](https://mlsanity.com/paper/2506.10751/) (2025)

## Built on by (most cited first, 1 of 1)

- [MatMMExtract: An Open-Source Pipeline for Panel-Level Extraction of Grounded Image-Text Pairs from Materials Science Literature](https://mlsanity.com/paper/2606.29667/) (2026)

## Abstract (arXiv)

Scientific figure captions require both accuracy and stylistic consistency to convey visual information. Here, we present a domain-specific caption generation system for the 3rd SciCap Challenge that integrates figure-related textual context with author-specific writing styles using the LaMP-Cap dataset. Our approach uses a two-stage pipeline: Stage 1 combines context filtering, category-specific prompt optimization via DSPy's MIPROv2 and SIMBA, and caption candidate selection; Stage 2 applies few-shot prompting with profile figures for stylistic refinement. Our experiments demonstrate that category-specific prompts outperform both zero-shot and general optimized approaches, improving ROUGE-1 recall by +8.3% while limiting precision loss to -2.8% and BLEU-4 reduction to -10.9%. Profile-informed stylistic refinement yields 40--48% gains in BLEU scores and 25--27% in ROUGE. Overall, our system demonstrates that combining contextual understanding with author-specific stylistic adaptation can generate captions that are both scientifically accurate and stylistically faithful to the source paper.