  Gender-inclusive NLP research has documented the harmful limitations of
gender binary-centric large language models (LLM), such as the inability to
correctly use gender-diverse English neopronouns (e.g., xe, zir, fae). While
data scarcity is a known culprit, the precise mechanisms through which scarcity
affects this behavior remain underexplored. We discover LLM misgendering is
significantly influenced by Byte-Pair Encoding (BPE) tokenization, the
tokenizer powering many popular LLMs. Unlike binary pronouns, BPE overfragments
neopronouns, a direct consequence of data scarcity during tokenizer training.
This disparate tokenization mirrors tokenizer limitations observed in
multilingual and low-resource NLP, unlocking new misgendering mitigation
strategies. We propose two techniques: (1) pronoun tokenization parity, a
method to enforce consistent tokenization across gendered pronouns, and (2)
utilizing pre-existing LLM pronoun knowledge to improve neopronoun proficiency.
Our proposed methods outperform finetuning with standard BPE, improving
neopronoun accuracy from 14.1% to 58.4%. Our paper is the first to link LLM
misgendering to tokenization and deficient neopronoun grammar, indicating that
LLMs unable to correctly treat neopronouns as pronouns are more prone to
misgender.
