EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models

arXiv:2402.11430v1 Announce Type: new
Abstract: In this study, we present EventRL, a reinforcement learning approach developed to enhance event extraction for large language models (LLMs). EventRL utilizes outcome supervision with specific reward functions to tackle prevalent challenges in LLMs, such as instruction following and hallucination, manifested as the mismatch of event structure and the generation of undefined event types. We evaluate EventRL against existing methods like Few-Shot Prompting (FSP) (based on GPT4) and Supervised Fine-Tuning (SFT) across various LLMs, including …

abstract arxiv challenges cs.cl event extraction functions hallucination language language models large language large language models llms outcome supervision reinforcement reinforcement learning study supervision type