Gender-inclusive language is often used with the aim of ensuring that all individuals, regardless of gender, can be associated with certain concepts. While psycholinguistic studies have examined its effects in relation to human cognition, it remains unclear how Large Language Models (LLMs) process gender-inclusive language. Given that commercial LLMs are gaining an increasingly strong foothold in everyday applications, it is crucial to examine whether LLMs in fact interpret gender-inclusive language neutrally, because the language they generate has the potential to influence the language of their users. This study examines whether LLM-generated coreferent terms align with a given gender expression or reflect model biases. Adapting psycholinguistic methods from French to English and German, we find that in English, LLMs generally maintain the antecedent's gender but exhibit underlying masculine bias. In German, this bias is much stronger, overriding all tested gender-neutralization strategies.

本研究解决了大型语言模型（LLMs）如何处理性别包容语言这一问题，填补了现有心理语言学研究与LLMs应用之间的空白。通过将心理语言学方法从法语转化为英语和德语，研究发现英语中的LLMs通常保持前指代的性别，但存在潜在的男性偏见，而在德语中这种偏见更为明显，超越了所有测试的性别中性化策略。该研究的重要发现揭示了LLMs生成的语言可能会影响用户语言的使用，强调了对模型的公正性和中立性的关注。 

将心理语言学研究应用于大型语言模型：核心指代语境中的性别包容语言