Large Language Models (LLMs) combined with instruction tuning have made significant progress when generalizing to unseen tasks. However, they have been less successful in Information Extraction (IE), lagging behind task-specific models. Typically, IE tasks are characterized by complex annotation guidelines which describe the task and give examples to humans. Previous attempts to leverage such information have failed, even with the largest models, as they are not able to follow the guidelines out-of-the-box. In this paper we propose GoLLIE (Guideline-following Large Language Model for IE), a model able to improve zero-shot results on unseen IE tasks by virtue of being fine-tuned to comply with annotation guidelines. Comprehensive evaluation empirically demonstrates that GoLLIE is able to generalize to and follow unseen guidelines, outperforming previous attempts at zero-shot information extraction. The ablation study shows that detailed guidelines is key for good results.

翻译：大语言模型（LLM）结合指令微调在泛化到未见任务方面取得了显著进展，但在信息抽取任务上仍落后于特定任务模型，表现欠佳。典型的信息抽取任务具有复杂的注释指南，这些指南向人类描述任务并提供示例。以往尝试利用此类信息的方法均未成功，即使采用最大规模的模型，也无法开箱即用地遵循这些指南。本文提出GoLLIE（遵循指南的大语言模型进行信息抽取），该模型通过微调以遵循注释指南，从而提升在未见信息抽取任务上的零样本结果。综合评估实证表明，GoLLIE能够泛化并遵循未见指南，优于以往零样本信息抽取的尝试。消融实验显示，详细的注释指南是实现良好效果的关键。