Structured Event Reasoning with Large Language Models
<|reference_start|>Structured Event Reasoning with Large Language Models: Reasoning about real-life events is a unifying challenge in AI and NLP that has profound utility in a variety of domains, while fallacy in high-stake applications could be catastrophic. Able to work with diverse text in these domains, large language models (LLMs) have proven capable of answering questions and solving problems. However, I show that end-to-end LLMs still systematically fail to reason about complex events, and they lack interpretability due to their black-box nature. To address these issues, I propose three general approaches to use LLMs in conjunction with a structured representation of events. The first is a language-based representation involving relations of sub-events that can be learned by LLMs via fine-tuning. The second is a semi-symbolic representation involving states of entities that can be predicted and leveraged by LLMs via few-shot prompting. The third is a fully symbolic representation that can be predicted by LLMs trained with structured data and be executed by symbolic solvers. On a suite of event reasoning tasks spanning common-sense inference and planning, I show that each approach greatly outperforms end-to-end LLMs with more interpretability. These results suggest manners of synergy between LLMs and structured representations for event reasoning and beyond.<|reference_end|>
@article{zhang2024structured,
    title={Structured Event Reasoning with Large Language Models},
    author={Li Zhang},
    journal={arXiv preprint arXiv:2408.16098},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16098},
    primaryClass={cs.CL}
}