@inproceedings{rykov-etal-2025-looking,
    title = "Through the Looking Glass: Common Sense Consistency Evaluation of Weird Images",
    author = "Rykov, Elisei  and
      Petrushina, Kseniia  and
      Titova, Kseniia  and
      Razzhigaev, Anton  and
      Panchenko, Alexander  and
      Konovalov, Vasily",
    editor = "Ebrahimi, Abteen  and
      Haider, Samar  and
      Liu, Emmy  and
      Haider, Sammar  and
      Leonor Pacheco, Maria  and
      Wein, Shira",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
    month = apr,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-06/2025.naacl-srw.28/",
    doi = "10.18653/v1/2025.naacl-srw.28",
    pages = "279--293",
    ISBN = "979-8-89176-192-6",
    abstract = "Measuring how real images look is a complex task in artificial intelligence research. For example, an image of Albert Einstein holding a smartphone violates common-sense because modern smartphone were invented after Einstein{'}s death. We introduce a novel method, which we called Through the Looking Glass (TLG), to assess image common sense consistency using Large Vision-Language Models (LVLMs) and Transformer-based encoder. By leveraging LVLM to extract atomic facts from these images, we obtain a mix of accurate facts. We proceed by fine-tuning a compact attention-pooling classifier over encoded atomic facts. Our TLG has achieved a new state-of-the-art performance on the WHOOPS! and WEIRD datasets while leveraging a compact fine-tuning component."
}