title: RONA: Pragmatically Diverse Image Captioning with Coherence Relations
summary: 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
paper_authors._id: 67e5528c3a023bdcabe85a47
paper_authors.name: Aashish Anantha Ramakrishnan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-27T14:25:14.389Z
paper_authors._id: 64b94d61d2176850e0d2cef2
paper_authors.avatarUrl: /avatars/b76903cc64b61d652b12b31d8832697d.svg
paper_authors.fullname: Aashish Anantha Ramakrishnan
paper_authors.type: user
paper_authors.user: aashananth
paper_authors._id: 67e5528c3a023bdcabe85a48
paper_authors.name: Aadarsh Anantha Ramakrishnan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-27T14:25:16.660Z
paper_authors._id: 65198e4a5ae78fd448f17f68
paper_authors.avatarUrl: /avatars/8ff77732afa9e0ab05ade2be45739130.svg
paper_authors.fullname: Aadarsh
paper_authors.type: user
paper_authors.user: aadarsh-ram
paper_authors._id: 67e5528c3a023bdcabe85a49
paper_authors.name: Dongwon Lee
paper_title: RONA: Pragmatically Diverse Image Captioning with Coherence Relations
paper_summary: 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
paper_ai_summary: RONA, a prompting strategy for Multi-modal Large Language Models, improves caption diversity and accuracy by using Coherence Relations.
paper_ai_keywords: Multi-modal Large Language Models MLLM Coherence Relations caption diversity ground-truth alignment