@inproceedings{samyoun-etal-2026-attribution,
    title = "Attribution-Guided Multi-Object Hallucination and Bias Detection in Vision-Language Models",
    author = "Samyoun, Sirat  and
      Xiao, Yingtai  and
      Du, Jian",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.210/",
    pages = "4529--4548",
    ISBN = "979-8-89176-380-7",
    abstract = "Vision-Language Models excel in multi-modal tasks but often hallucinate objects or exhibit linguistic bias by over-repeating object names, especially in complex multi-object scenes. Existing methods struggle with multi-object grounding because language priors frequently dominate visual evidence, causing hallucinated or biased objects to produce attention distributions or similarity scores nearly indistinguishable from those of real objects. We introduce SHAPLENS, a Shapley value{--}based attribution framework using Kernel SHAP and multi-layer fusion to detect hallucinated and biased objects. Evaluated on ADE and COCO datasets across four leading VLMs, SHAPLENS improves hallucination detection accuracy by 8{--}12{\%} and F1 by 10{--}14{\%} over the best baselines. It also achieves up to 6{\%} higher bias detection performance across three distinct bias types on a curated HQH benchmark and exhibits minimal degradation ({\ensuremath{<}}0.03{\%}) across partial and perturbed contexts."
}