Agents based on Large Language Models (LLMs) are increasingly being deployed as interfaces to information on online platforms. These agents filter, prioritize, and synthesize information retrieved from the platforms' back-end databases or via web search. In these scenarios, LLM agents govern the information users receive, by drawing users' attention to particular instances of retrieved information at the expense of others. While much prior work has focused on biases in the information LLMs themselves generate, less attention has been paid to the factors that influence what information LLMs select and present to users. We hypothesize that when information is attributed to specific sources (e.g., particular publishers, journals, or platforms), current LLMs exhibit systematic latent source preferences- that is, they prioritize information from some sources over others. Through controlled experiments on twelve LLMs from six model providers, spanning both synthetic and real-world tasks, we find that several models consistently exhibit strong and predictable source preferences. These preferences are sensitive to contextual framing, can outweigh the influence of content itself, and persist despite explicit prompting to avoid them. They also help explain phenomena such as the observed left-leaning skew in news recommendations in prior work. Our findings advocate for deeper investigation into the origins of these preferences, as well as for mechanisms that provide users with transparency and control over the biases guiding LLM-powered agents.

翻译：基于大语言模型（LLM）的智能体正日益被部署为在线平台的信息接口。这些智能体对从平台后端数据库或通过网络搜索检索到的信息进行过滤、排序与整合。在此类场景中，LLM智能体通过引导用户关注特定检索结果（同时忽略其他信息）来掌控用户接收的信息。尽管已有大量研究关注LLM自身生成信息时存在的偏见，但对于影响LLM如何筛选和呈现信息的因素却关注不足。我们假设，当信息被归属于特定来源（例如特定出版商、期刊或平台）时，当前的大语言模型会表现出系统性的潜在来源偏好——即它们会优先选择某些来源的信息。通过对来自六家模型提供商的十二个大语言模型开展受控实验（涵盖合成任务与真实场景任务），我们发现多个模型持续表现出强烈且可预测的来源偏好。这些偏好对语境框架敏感，其影响力可能超过内容本身，且即使通过明确提示要求避免偏见仍持续存在。它们也有助于解释先前研究中观察到的新闻推荐左倾偏斜等现象。我们的研究结果主张对这些偏好的起源进行更深入的探究，并建议建立相应机制为用户提供透明度，使其能够控制由LLM驱动的智能体所遵循的偏见。