Revisiting subword tokenization: A case study on affixal negation in large language models
Thinh Truong, Yulia Otmakhova, Karin Verspoor, Trevor Cohn, Timothy Baldwin
Abstract
In this work, we measure the impact of affixal negation on modern English large language models (LLMs). In affixal negation, the negated meaning is expressed through a negative morpheme, which is potentially challenging for LLMs as their tokenizers are often not morphologically plausible. We conduct extensive experiments using LLMs with different subword tokenization methods, which lead to several insights on the interaction between tokenization performance and negation sensitivity. Despite some interesting mismatches between tokenization accuracy and negation detection performance, we show that models can, on the whole, reliably recognize the meaning of affixal negation.
BibTeX
@inproceedings{truong-etal-2024-revisiting,
title = "Revisiting subword tokenization: A case study on affixal negation in large language models",
author = "Truong, Thinh and
Otmakhova, Yulia and
Verspoor, Karin and
Cohn, Trevor and
Baldwin, Timothy",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.284/",
doi = "10.18653/v1/2024.naacl-long.284",
pages = "5082--5095"
}