GuideX: Guided Synthetic Data Generation for Zero-Shot Information
  Extraction
Authors: name: Neil De La Fuente, name: Oscar Sainz, name: Iker García-Ferrero, name: Eneko Agirre

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