@inproceedings{huang-caragea-2026-madiave,
    title = "{MADIAVE}: Multi-Agent Debate for Implicit Attribute Value Extraction",
    author = "Huang, Wei-Chieh  and
      Caragea, Cornelia",
    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.159/",
    pages = "3035--3053",
    ISBN = "979-8-89176-386-9",
    abstract = "Implicit Attribute Value Extraction (AVE) is essential for accurately representing products in e-commerce, as it infers lantent attributes from multimodal data. Despite advances in multimodal large language models (MLLMs), implicit AVE remains challenging due to the complexity of multidimensional data and gaps in vision-text understanding. In this work, we introduce MADIAVE, a multi-agent de- bate framework that employs multiple MLLM agents to iteratively refine inferences. Through a series of debate rounds, agents verify and up- date each other{'}s responses, thereby improving inference performance and robustness. Experi- ments on the ImplicitAVE dataset demonstrate that even a few rounds of debate significantly boost accuracy, especially for attributes with initially low performance. We systematically evaluate various debate configurations, includ- ing identical or different MLLM agents, and analyze how debate rounds affect convergence dynamics. Our findings highlight the poten- tial of multi-agent debate strategies to address the limitations of single-agent approaches and offer a scalable solution for implicit AVE in multimodal e-commerce."
}