LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure
  Profiles
Authors: name: Ho Yin 'Sam' Ng, name: Ting-Yao Hsu, name: Aashish Anantha Ramakrishnan, name: Branislav Kveton, name: Nedim Lipka, name: Franck Dernoncourt, name: Dongwon Lee, name: Tong Yu, name: Sungchul Kim, name: Ryan A. Rossi, name: Ting-Hao 'Kenneth' Huang

Figure captions are crucial for helping readers understand and remember a
figure's key message. Many models have been developed to generate these
captions, helping authors compose better quality captions more easily. Yet,
authors almost always need to revise generic AI-generated captions to match
their writing style and the domain's style, highlighting the need for
personalization. Despite language models' personalization (LaMP) advances,
these technologies often focus on text-only settings and rarely address
scenarios where both inputs and profiles are multimodal. This paper introduces
LaMP-Cap, a dataset for personalized figure caption generation with multimodal
figure profiles. For each target figure, LaMP-Cap provides not only the needed
inputs, such as figure images, but also up to three other figures from the same
document--each with its image, caption, and figure-mentioning paragraphs--as a
profile to characterize the context. Experiments with four LLMs show that using
profile information consistently helps generate captions closer to the original
author-written ones. Ablation studies reveal that images in the profile are
more helpful than figure-mentioning paragraphs, highlighting the advantage of
using multimodal profiles over text-only ones.