As libraries explore large language models (LLMs) for use in virtual reference services, a key question arises: Can LLMs serve all users equitably, regardless of demographics or social status? While they offer great potential for scalable support, LLMs may also reproduce societal biases embedded in their training data, risking the integrity of libraries' commitment to equitable service. To address this concern, we evaluate whether LLMs differentiate responses across user identities by prompting six state-of-the-art LLMs to assist patrons differing in sex, race/ethnicity, and institutional role. We find no evidence of differentiation by race or ethnicity, and only minor evidence of stereotypical bias against women in one model. LLMs demonstrate nuanced accommodation of institutional roles through the use of linguistic choices related to formality, politeness, and domain-specific vocabularies, reflecting professional norms rather than discriminatory treatment. These findings suggest that current LLMs show a promising degree of readiness to support equitable and contextually appropriate communication in academic library reference services.

翻译：随着图书馆探索在虚拟参考服务中使用大型语言模型（LLMs），一个关键问题随之产生：LLMs能否公平地为所有用户服务，无论其人口特征或社会地位如何？尽管LLMs为可扩展的支持提供了巨大潜力，但它们也可能重现训练数据中嵌入的社会偏见，从而危及图书馆对公平服务承诺的完整性。为解决这一关切，我们通过提示六种最先进的LLMs为不同性别、种族/民族和机构角色的用户提供帮助，评估LLMs是否因用户身份差异而提供不同的回答。我们发现没有证据表明LLMs会因种族或民族而区别对待，仅在一个模型中观察到对女性存在轻微的刻板印象偏见。LLMs通过使用与正式性、礼貌性和领域特定词汇相关的语言选择，展现出对机构角色的细致适应，这反映了专业规范而非歧视性对待。这些发现表明，当前的LLMs在支持学术图书馆参考咨询服务中实现公平且情境适宜的沟通方面，展现出令人期待的成熟度。