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    <abstract>Zero-shot information extraction (IE) with large language models (LLMs) enables adaptation to new schemas and domains without task-specific training. Existing methods mainly follow three paradigms. Monolithic prompting is efficient but prone to missed mentions, boundary errors, and type confusion. Each-type prompting improves type-level focus but may produce overlapping or conflicting predictions, while multi-agent debate can resolve such conflicts at the cost of irrelevant context, redundant interactions, and high token overhead. To address these issues, we propose SMADE-IE, a sparse and evidence-driven multi-agent framework. An Adaptive Mode Selector routes simple inputs to a lightweight Global Extraction Mode and ambiguous inputs to a Type-Centric Extraction Mode based on sample complexity and relevant types. Cross-type conflicts are resolved by an Evidence-Driven Debate module that uses Toulmin-style arguments, external evidence scoring, Beta-based confidence updates, and early stopping. Experiments on nine benchmarks covering NER, RE, and JERE show that MADE-IE improves average Partial F1 over the strongest baselines by 11.37, 3.83, and 14.46 points, respectively. Compared with the multi-agent baseline CrossAgentIE, SMADE-IE reduces token consumption by 85.1% on DocRED and 80.1% on CrossRE, demonstrating substantially higher inference efficiency. Code is available at https://github.com/Cppys/SMADE-IE.</abstract>
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%0 Conference Proceedings
%T SMADE-IE: Sparse Multi-Agent Framework with Evidence-Driven Debate for Zero-Shot Information Extraction
%A Huang, Kenfeng
%A Cai, Yi
%A Wu, Xin
%A Deng, Zikun
%A Yuan, L. I.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F huang-etal-2026-smade
%X Zero-shot information extraction (IE) with large language models (LLMs) enables adaptation to new schemas and domains without task-specific training. Existing methods mainly follow three paradigms. Monolithic prompting is efficient but prone to missed mentions, boundary errors, and type confusion. Each-type prompting improves type-level focus but may produce overlapping or conflicting predictions, while multi-agent debate can resolve such conflicts at the cost of irrelevant context, redundant interactions, and high token overhead. To address these issues, we propose SMADE-IE, a sparse and evidence-driven multi-agent framework. An Adaptive Mode Selector routes simple inputs to a lightweight Global Extraction Mode and ambiguous inputs to a Type-Centric Extraction Mode based on sample complexity and relevant types. Cross-type conflicts are resolved by an Evidence-Driven Debate module that uses Toulmin-style arguments, external evidence scoring, Beta-based confidence updates, and early stopping. Experiments on nine benchmarks covering NER, RE, and JERE show that MADE-IE improves average Partial F1 over the strongest baselines by 11.37, 3.83, and 14.46 points, respectively. Compared with the multi-agent baseline CrossAgentIE, SMADE-IE reduces token consumption by 85.1% on DocRED and 80.1% on CrossRE, demonstrating substantially higher inference efficiency. Code is available at https://github.com/Cppys/SMADE-IE.
%U https://aclanthology.org/2026.emnlp-main.1381/
%P 31532-31554

Markdown (Informal)

[SMADE-IE: Sparse Multi-Agent Framework with Evidence-Driven Debate for Zero-Shot Information Extraction](https://aclanthology.org/2026.emnlp-main.1381/) (Huang et al., EMNLP 2026)

• SMADE-IE: Sparse Multi-Agent Framework with Evidence-Driven Debate for Zero-Shot Information Extraction (Huang et al., EMNLP 2026)

ACL

• Kenfeng Huang, Yi Cai, Xin Wu, Zikun Deng, and LI Yuan. 2026. SMADE-IE: Sparse Multi-Agent Framework with Evidence-Driven Debate for Zero-Shot Information Extraction. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 31532–31554, Budapest, Hungary. Association for Computational Linguistics.