EventRL: Enhancing Event Extraction with Outcome Supervision for Large
  Language Models
Jun Gao Huan Zhao Wei Wang Changlong Yu Ruifeng Xu
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
GPT-4, LLaMa, and CodeLLaMa models. Our findings show that EventRL
significantly outperforms these conventional approaches by improving the
performance in identifying and structuring events, particularly in handling
novel event types. The study emphasizes the critical role of reward function
selection and demonstrates the benefits of incorporating code data for better
event extraction. While increasing model size leads to higher accuracy,
maintaining the ability to generalize is essential to avoid overfitting.