用自动生成标注数据提升零样本信息抽取的跨领域泛化能力
GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction
- 通过自动定义领域标签规范并生成合成标注数据
- 在7个零样本命名实体识别任务上达到新最佳，最高提升7 F1点
- 无需人工标注即可实现强泛化，适合快速部署到新领域
信息抽取系统传统上依赖特定领域，需耗费成本进行模式设计、数据标注和模型训练。尽管大语言模型在零样本信息抽取中展现潜力，但在标签定义不同的未见领域中性能显著下降。本文提出GUIDEX，一种新方法：自动定义领域特定模式、推断标注规则，并生成合成标注实例，从而增强跨领域泛化能力。使用GUIDEX微调Llama 3.1，在七个零样本命名实体识别基准上达到新最优表现。仅使用合成数据训练的模型相比此前方法最高提升7 F1点，结合真实标注数据时仍高出近2 F1点。训练模型对复杂领域标注模式的理解能力显著增强。代码、模型与合成数据集可在neilus03.github.io/guidex.com获取。
原文摘要 · Abstract (English)
Information Extraction (IE) systems are traditionally domain-specific, requiring costly adaptation that involves expert schema design, data annotation, and model training. While Large Language Models have shown promise in zero-shot IE, performance degrades significantly in unseen domains where label definitions differ. This paper introduces GUIDEX, a novel method that automatically defines domain-specific schemas, infers guidelines, and generates synthetically labeled instances, allowing for better out-of-domain generalization. Fine-tuning Llama 3.1 with GUIDEX sets a new state-of-the-art across seven zeroshot Named Entity Recognition benchmarks. Models trained with GUIDEX gain up to 7 F1 points over previous methods without humanlabeled data, and nearly 2 F1 points higher when combined with it. Models trained on GUIDEX demonstrate enhanced comprehension of complex, domain-specific annotation schemas. Code, models, and synthetic datasets are available at neilus03.github.io/guidex.com
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