EvEntS ReaLM: Event Reasoning of Entity States via Language Models

Understand

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.