Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Yang Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

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.1

Advances in word representations have shown tremendous improvements in downstream NLP tasks, but lack semantic interpretability.In this paper 1 , we introduce Definition Frames (DF), a matrix distributed representation extracted from definitions, where each dimension is semantically interpretable.DF dimensions correspond to the Qualia structure relations (Boguraev and Pustejovsky, 1990): a set of relations that uniquely define a term.Our results show that DFs have competitive performance with other distributional semantic approaches on word similarity tasks.