  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.
