@inproceedings{kim-etal-2026-visual,
    title = "Visual{--}Linguistic Abductive Reasoning with {LLM}s for Knowledge-based Visual Question Answering",
    author = "Kim, Jieun  and
      Jeong, Yujin  and
      Cho, Sung-Bae",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.343/",
    pages = "6529--6544",
    ISBN = "979-8-89176-386-9",
    abstract = "Recent attempts to leverage large language models (LLMs) for reasoning and pre-trained knowledge in multi-modal reasoning focus on two main approaches: aligning image features with linguistic space, and converting images into textual cues to exploit the implicit reasoning capabilities of LLMs. Although they integrate visual information into the reasoning pipeline, they often treat visual perception and language reasoning as separate processes, limiting the potential for fully unified multi-modal reasoning. In this paper, we propose a novel method, Visual{--}Linguistic Abductive Reasoning (ViLA), inspired by human abductive reasoning processes. ViLA hypothesizes a plausible answer, generates the corresponding visual and textual premises, and employs fuzzy scoring to select the most coherent combination, thus deriving the final inference. This process integrates visual and linguistic modalities into interpretable abductive reasoning chains, enabling unified multi-modal reasoning. Without fine-tuning LLMs or retrieving external knowledge, ViLA improves performance by 2.31{\%} on AOKVQA, 1.7{\%} on OKVQA, and 1.7{\%} on GQA over previous state-of-the-art models, while also improving interpretability and stability."
}