@inproceedings{xie-etal-2025-dape,
    title = "{DAPE}-{BR}: Distance-Aware Positional Encoding for Mitigating Object Hallucination in {LVLM}s",
    author = "Xie, Mingrui  and
      Xu, Tianxiang  and
      Tang, Qianhai  and
      Yao, Shanming  and
      Zhang, Xiaofeng  and
      Du, Junliang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.459/",
    doi = "10.18653/v1/2025.findings-emnlp.459",
    pages = "8638--8649",
    ISBN = "979-8-89176-335-7",
    abstract = "Large Vision{--}Language Models (LVLMs) have garnered substantial interest owing to their impressive ability to interpret visual inputs and converse with users.Nevertheless, LVLMs still suffer from object hallucination {--} generating descriptions for objects that are absent from the image, which undermines reliability and hinders real-world deployment. We propose DAPE-BR, a positional-alignment scheme that (i) preserves the pretrained weight order while globally{---}- visual{--}text distances, (ii) embeds an isotropic fused patch-distance metric, and (iii) applies a patch-distance causal mask to enforce spatial causality. Extensive experiments on POPE, MMStar and SQA show that DAPE-BR consistently reduces hallucinations and boosts."
}