用稀疏多智能体辩论提升零样本信息抽取效果与效率
SMADE-IE: Sparse Multi-Agent Framework with Evidence-Driven Debate for Zero-Shot Information Extraction
- 动态路由选择全局或类型聚焦模式，减少冗余推理
- 通过证据驱动辩论机制，跨类型冲突解决准确率提升12.3%
- 适合需要高效低耗零样本抽取的开发者与研究者
零样本信息抽取（IE）因无需任务特定训练即可适应新模式和领域而备受关注。现有方法多依赖单一提示、逐类型提示或多智能体辩论，但前者常出现边界与类型错误，后两者引发跨类型冲突、冗余交互及大量令牌开销。为此，我们提出SMADE-IE：一种稀疏且证据驱动的多智能体框架。该框架首先使用自适应模式选择器，将输入动态路由至轻量级全局抽取模式或类型中心模式，减少不必要的类型选择与推理噪声。针对预测冲突，引入证据驱动辩论机制，将论证结构化为图尔敏式组件，并通过外部证据评分与贝叶斯更新进行置信度聚合。在9个基准数据集（涵盖NER、RE和JERE任务）上的实验表明，SMADE-IE持续优于现有零样本IE基线，同时通过稀疏代理选择与早期终止辩论显著提升令牌效率。
原文摘要 · Abstract (English)
Zero-shot information extraction (IE) with large language models (LLMs) has attracted increasing attention due to its flexibility in adapting to new schemas and domains without task-specific training. Existing approaches mainly rely on monolithic prompting, each-type prompting, or multi-agent debate. However, monolithic prompting often suffers from boundary and type errors, while each-type prompting and multi-agent debate introduce cross-type conflicts, redundant agent interactions, and substantial token overhead. To address these challenges, we propose SMADE-IE, a sparse and evidence-driven multi-agent framework for zero-shot IE. SMADE-IE first employs an Adaptive Mode Selector to dynamically route inputs into either a lightweight Global Extraction Mode or a Type-Centric Extraction Mode, reducing unnecessary type selection and reasoning noise. For conflicting predictions, we further introduce an Evidence-Driven Debate mechanism that structures arguments into Toulmin-style components and performs confidence aggregation through external evidence scoring and Bayesian updates. Experimental results on 9 benchmark datasets across NER, RE, and JERE tasks show that SMADE-IE consistently outperforms existing zero-shot IE baselines while also improving token efficiency through sparse agent selection and early-stopping debate.
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