A Comprehensive Evaluation on Event Reasoning of Large Language Models
Zhengwei Tao, Zhi Jin, Yifan Zhang, Xiancai Chen, Haiyan Zhao, Jia Li, Bin Liang, Chongyang Tao
Abstract
Event reasoning is a fundamental ability that underlies many applications. It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of the inter-event relations and the reasoning paradigms. The extent to which LLMs excel in event reasoning across various relations and reasoning paradigms has not been thoroughly investigated. Additionally, it is still unclear whether LLMs utilize event knowledge in the same way humans do. To mitigate this disparity, we comprehensively evaluate the abilities of event reasoning of LLMs on different relations, paradigms, and levels of abstraction. We introduce a novel benchmark EV2 for EValuation of EVent reasoning. EV2 consists of two levels of evaluation on schema and instance and is comprehensive in relations and reasoning paradigms. We conduct extensive experiments on EV2. We find that 1) LLMs have abilities to accomplish event reasoning but their performances are far from satisfactory. 2) There are imbalances of event reasoning abilities on different relations and paradigms. 3) LLMs have event schema knowledge, however, they're not aligned with humans on how to utilize the knowledge. Based on these findings, we guide the LLMs in utilizing the event schema knowledge as memory leading to improvements in event reasoning.
BibTeX
@article{Tao_Jin_Zhang_Chen_Zhao_Li_Liang_Tao_Liu_Wong_2025, title={A Comprehensive Evaluation on Event Reasoning of Large Language Models}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/34714}, DOI={10.1609/aaai.v39i24.34714}, abstractNote={Event reasoning is a fundamental ability that underlies many applications. It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of the inter-event relations and the reasoning paradigms. The extent to which LLMs excel in event reasoning across various relations and reasoning paradigms has not been thoroughly investigated. Additionally, it is still unclear whether LLMs utilize event knowledge in the same way humans do. To mitigate this disparity, we comprehensively evaluate the abilities of event reasoning of LLMs on different relations, paradigms, and levels of abstraction. We introduce a novel benchmark EV2 for EValuation of EVent reasoning. EV2 consists of two levels of evaluation on schema and instance and is comprehensive in relations and reasoning paradigms. We conduct extensive experiments on EV2. We find that 1) LLMs have abilities to accomplish event reasoning but their performances are far from satisfactory. 2) There are imbalances of event reasoning abilities on different relations and paradigms. 3) LLMs have event schema knowledge, however, they’re not aligned with humans on how to utilize the knowledge. Based on these findings, we guide the LLMs in utilizing the event schema knowledge as memory leading to improvements in event reasoning.}, number={24}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Tao, Zhengwei and Jin, Zhi and Zhang, Yifan and Chen, Xiancai and Zhao, Haiyan and Li, Jia and Liang, Bin and Tao, Chongyang and Liu, Qun and Wong, Kam-Fai}, year={2025}, month={Apr.}, pages={25273-25281} }