研究大模型如何理解性别中立语言，发现其存在隐性男性偏见。
Adapting Psycholinguistic Research for LLMs: Gender-inclusive Language in a Coreference Context
- 借鉴心理学实验方法，测试大模型对性别中立表达的处理机制。
- 英语模型虽保留指代性别，但倾向男性；德语模型偏差更强且无法消除。
- 揭示商用大模型在性别表达上的潜在偏见，适合关注AI伦理的研究者。
性别中立语言旨在确保所有个体无论性别都能与特定概念关联。尽管心理学研究已探讨其对人类认知的影响，但大型语言模型（LLMs）如何处理此类语言仍不明确。鉴于商业大模型在日常应用中日益普及，评估其是否真正中立地理解性别中立语言至关重要，因为其生成的语言可能影响用户。本研究考察大模型生成的回指项是否与先行词的性别一致，或反映模型偏见。通过将法语心理语言学方法适配至英语和德语，发现英语模型通常保持先行词的性别，但存在潜在的男性偏见；德语模型中该偏见更为显著，甚至压倒了所有测试过的性别中性化策略。
原文摘要 · Abstract (English)
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.
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