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：大型语言模型（LLM）结合指令微调在泛化至未见任务上取得了显著进展，但在信息抽取（IE）领域表现欠佳，落后于特定任务模型。典型的信息抽取任务需要复杂的标注指南，其中描述任务并给出人工示例。此前利用此类信息的尝试均告失败——即使使用最大规模的模型，也无法直接遵循标注指南。本文提出GoLLIE（遵循指南的信息抽取大型语言模型），该模型通过微调以遵守标注指南，从而提升在未见信息抽取任务上的零样本效果。综合评估实证表明，GoLLIE能够泛化并遵循未见标注指南，性能优于此前零样本信息抽取的尝试。消融研究显示，详细的标注指南是实现良好结果的关键。