GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction

arXiv:2310.03668v4 Announce Type: replace
Abstract: 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 …

abstract annotation arxiv cs.cl examples extraction guidelines humans information information extraction language language models large language large language models llms progress tasks type zero-shot