@inproceedings{anantha-ramakrishnan-etal-2025-rona,
    title = "{RONA}: Pragmatically Diverse Image Captioning with Coherence Relations",
    author = "Anantha Ramakrishnan, Aashish  and
      Ramakrishnan, Aadarsh Anantha  and
      Lee, Dongwon",
    editor = "Padmakumar, Vishakh  and
      Gero, Katy  and
      Wambsganss, Thiemo  and
      Sterman, Sarah  and
      Huang, Ting-Hao  and
      Zhou, David  and
      Chung, John",
    booktitle = "Proceedings of the Fourth Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, US",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.in2writing-1.8/",
    pages = "74--86",
    ISBN = "979-8-89176-239-8",
    abstract = "Writing Assistants (e.g., Grammarly, Microsoft Copilot) traditionally generate diverse image captions by employing syntactic and semantic variations to describe image components. However, human-written captions prioritize conveying a central message alongside visual descriptions using pragmatic cues. To enhance caption diversity, it is essential to explore alternative ways of communicating these messages in conjunction with visual content. We propose RONA, a novel prompting strategy for Multi-modal Large Language Models (MLLM) that leverages Coherence Relations as a controllable axis for pragmatic variations. We demonstrate that RONA generates captions with better overall diversity and ground-truth alignment, compared to MLLM baselines across multiple domains. Our code is available at: https://github.com/aashish2000/RONA"
}