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    <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.</abstract>
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%0 Conference Proceedings
%T MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extraction
%A Huang, Wei-Chieh
%A Caragea, Cornelia
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F huang-caragea-2026-madiave
%X 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.
%U https://aclanthology.org/2026.findings-eacl.159/
%P 3035-3053

Markdown (Informal)

[MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extraction](https://aclanthology.org/2026.findings-eacl.159/) (Huang & Caragea, Findings 2026)

• MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extraction (Huang & Caragea, Findings 2026)

ACL

• Wei-Chieh Huang and Cornelia Caragea. 2026. MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extraction. In Findings of the Association for Computational Linguistics: EACL 2026, pages 3035–3053, Rabat, Morocco. Association for Computational Linguistics.