Large language models (LLMs) are increasingly used for text generation tasks from everyday use to high-stakes enterprise and government applications, including simulated interviews with asylum seekers. While many works highlight the new potential applications of LLMs, there are risks of LLMs encoding and perpetuating harmful biases about non-dominant communities across the globe. To better evaluate and mitigate such harms, more research examining how LLMs portray diverse individuals is needed. In this work, we study how national origin identities are portrayed by widely-adopted LLMs in response to open-ended narrative generation prompts. Our findings demonstrate the presence of persistent representational harms by national origin, including harmful stereotypes, erasure, and one-dimensional portrayals of Global Majority identities. Minoritized national identities are simultaneously underrepresented in power-neutral stories and overrepresented in subordinated character portrayals, which are over fifty times more likely to appear than dominant portrayals. The degree of harm is amplified when US nationality cues (e.g., ``American'') are present in input prompts. Notably, we find that the harms we identify cannot be explained away via sycophancy, as US-centric biases persist even when replacing US nationality cues with non-US national identities in the prompts. Based on our findings, we call for further exploration of cultural harms in LLMs through methodologies that center Global Majority perspectives and challenge the uncritical adoption of US-based LLMs for the classification, surveillance, and misrepresentation of the majority of our planet.

翻译：大型语言模型（LLMs）日益被用于从日常使用到高风险企业和政府应用的文本生成任务，包括对寻求庇护者的模拟面谈。尽管许多研究强调了LLMs的新潜在应用，但存在LLMs编码并延续针对全球非主导群体有害偏见的风险。为更好地评估和缓解此类伤害，需要更多研究探讨LLMs如何描绘不同个体。在本工作中，我们研究了广泛采用的LLMs在回应开放式叙事生成提示时，如何描绘国家起源身份。我们的发现表明，针对国家起源存在持续的表征性伤害，包括有害刻板印象、抹除以及对全球多数民族身份的单一维度描绘。少数族裔国家身份在权力中性叙事中的代表性不足，同时在从属角色描绘中的代表性过度——后者出现的可能性是主导性描绘的五十倍以上。当输入提示中出现美国国籍线索（如"美国人"）时，伤害程度进一步加剧。值得注意的是，我们发现所识别的伤害无法通过谄媚效应解释，因为即便将提示中的美国国籍线索替换为非美国国家身份，以美国为中心的偏见依然存在。基于我们的发现，我们呼吁通过聚焦全球多数民族视角的方法论，进一步探索LLMs中的文化伤害，并质疑不加批判地将基于美国的LLMs用于对我们星球上多数人群进行分类、监视及错误表征的做法。