  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
that 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 are key for good results.
