@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",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T EvEntS ReaLM: Event Reasoning of Entity States via Language Models
%A Spiliopoulou, Evangelia
%A Pagnoni, Artidoro
%A Bisk, Yonatan
%A Hovy, Eduard
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F spiliopoulou-etal-2022-events
%X 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.
%R 10.18653/v1/2022.emnlp-main.129
%U https://aclanthology.org/2022.emnlp-main.129
%U https://doi.org/10.18653/v1/2022.emnlp-main.129
%P 1982-1997
Markdown (Informal)
[EvEntS ReaLM: Event Reasoning of Entity States via Language Models](https://aclanthology.org/2022.emnlp-main.129) (Spiliopoulou et al., EMNLP 2022)
ACL
- Evangelia Spiliopoulou, Artidoro Pagnoni, Yonatan Bisk, and Eduard Hovy. 2022. EvEntS ReaLM: Event Reasoning of Entity States via Language Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1982–1997, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.