RONA: Pragmatically Diverse Image Captioning with Coherence Relations
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
67e5528c3a023bdcabe85a47
Aashish Anantha Ramakrishnan
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2025-03-27T14:25:14.389Z
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Aashish Anantha Ramakrishnan
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aashananth
67e5528c3a023bdcabe85a48
Aadarsh Anantha Ramakrishnan
claimed_verified
2025-03-27T14:25:16.660Z
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/avatars/8ff77732afa9e0ab05ade2be45739130.svg
Aadarsh
user
aadarsh-ram
67e5528c3a023bdcabe85a49
Dongwon Lee
RONA: Pragmatically Diverse Image Captioning with Coherence Relations
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
RONA, a prompting strategy for Multi-modal Large Language Models, improves caption diversity and accuracy by using Coherence Relations.
Multi-modal Large Language Models MLLM Coherence Relations caption diversity ground-truth alignment