SMADE-IE: Sparse Multi-Agent Framework with Evidence-Driven Debate for Zero-Shot Information Extraction

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 SMADE-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.