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 lightweigh...
| Authors | Kenfeng Huang, Yi Cai, Xin Wu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 June 2026 |
| Last updated at source | 5 October 2026 |
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