Evaluating Generative Language Models in Information Extraction as Subjective Question Correction

arXiv:2404.03532v1 Announce Type: new
Abstract: Modern Large Language Models (LLMs) have showcased remarkable prowess in various tasks necessitating sophisticated cognitive behaviors. Nevertheless, a paradoxical performance discrepancy is observed, where these models underperform in seemingly elementary tasks like relation extraction and event extraction due to two issues in conventional evaluation. (1) The imprecision of existing evaluation metrics that struggle to effectively gauge semantic consistency between model outputs and ground truth, and (2) The inherent incompleteness of evaluation benchmarks, primarily due to …

arxiv cs.cl extraction generative information information extraction language language models question type