@inproceedings{kundu-etal-2026-carve,
    title = "{C}a{RVE}: Critiquing and Refining Visual Elaborations for Figurative Language Illustrations",
    author = "Kundu, Manishit  and
      Padole, Tejomay Kishor  and
      Shekhar, Sumit  and
      Banerjee, Biplab  and
      Bhattacharyya, Pushpak",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.2122/",
    pages = "42760--42777",
    ISBN = "979-8-89176-395-1",
    abstract = "Illustrating figurative language remains challenging due to its non-literal semantics, and existing text-to-image frameworks rely heavily on proprietary models or human supervision to achieve adequate alignment. We introduce CaRVE, a lightweight and fully open-source critique-driven framework that employs VLM feedback to refine visual elaborations for figurative image generation. CaRVE bridges the semantic alignment gap even in sub-4B models by correcting visual and conceptual misalignments, reducing over-literalization, and improving robustness to complex figurative expressions. Using only open-source models, CaRVE achieves a 6.49{\%} improvement over prior baselines on intrinsic automatic evaluations and a +0.37 average rank gain in human preference. We further release MetaCaRVE, an enhanced figurative image dataset constructed by refining HAIVMet using CaRVE."
}