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.

本文旨在研究测量后缀否定对现代英语大型语言模型（LLMs）的影响。通过负性语素表达被否定的含义，这对LLMs来说可能会具有挑战性，因为它们的分词器往往不具备形态的可行性。通过使用不同的子词分词方法，我们进行了大量实验，得出了有关分词性能和否定敏感性之间相互作用的几个见解。尽管分词准确性和否定检测性能之间存在一些有趣的不匹配，但我们证明模型总体上能够可靠地识别后缀否定的含义。

重温次词标记化：大型语言模型中的词缀否定案例研究