RONA: Pragmatically Diverse Image Captioning with Coherence Relations
Authors: name: Aashish Anantha Ramakrishnan, name: Aadarsh Anantha Ramakrishnan, name: Dongwon Lee

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 pragmatic diversity, it is essential to explore alternative ways of
communicating these messages in conjunction with visual content. To address
this challenge, we propose RONA, a novel prompting strategy for Multi-modal
Large Language Models (MLLM) that leverages Coherence Relations as an axis for
variation. 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