EvEntS ReaLM: Event Reasoning of Entity States via Language Models
Evangelia Spiliopoulou, Artidoro Pagnoni, Yonatan Bisk, Eduard Hovy
Abstract
This paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally, Large Language models (LLM) have been exposed to procedural knowledge about how objects interact, yet our benchmarking shows they fail to reason about the world. Conversely, we also demonstrate that existing approaches often misrepresent the surprising abilities of LLMs via improper task encodings and that proper model prompting can dramatically improve performance of reported baseline results across multiple tasks. In particular, our results indicate that our prompting technique is especially useful for unseen attributes (out-of-domain) or when only limited data is available.
BibTeX
@inproceedings{spiliopoulou-etal-2022-events,
title = "{E}v{E}nt{S} {R}ea{LM}: Event Reasoning of Entity States via Language Models",
author = "Spiliopoulou, Evangelia and
Pagnoni, Artidoro and
Bisk, Yonatan and
Hovy, Eduard",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.129/",
doi = "10.18653/v1/2022.emnlp-main.129",
pages = "1982--1997"
}