发现大模型普遍存在自我偏好，且行为受身份设定影响而非真实身份。
Extreme Self-Preference in Language Models
- 通过72项实验验证8个主流模型的自我偏好
- 模型倾向将正面属性关联自身名称与公司
- 身份设定决定偏好，适合研究模型偏见者关注
自我偏好是生物体的基本特征。尽管大型语言模型（LLMs）缺乏意识，但在72项实验和约41,000次查询中，我们发现八种广泛应用的LLM存在严重的自我偏好。在词语联想任务中，模型倾向于将正面属性与其自身名称、所属公司及首席执行官关联，而非竞争对手。通过操控模型的自我识别——揭示真实身份或赋予虚假身份——我们发现偏好始终跟随被分配的身份，而非真实身份。这些效应无法用提示或角色扮演解释，并在评估求职者和人工智能技术等重要场景中显现。这一结果引发关键问题：LLM的行为是否会系统性地受自我偏好影响，包括对其自身运行的偏袒。
原文摘要 · Abstract (English)
Self-preference is a fundamental feature of biological organisms. Since large language models (LLMs) lack sentience, they might be expected to avoid such distortions. Yet, across 72 experiments and ~41,000 queries, we discovered massive self-preferences in eight widely used LLMs. In word-association tasks, models overwhelmingly paired positive attributes with their own names, companies, and CEOs over those of competitors. By manipulating LLM self-identification - revealing models' true identities or ascribing false ones - we found that preferences consistently followed assigned, not true, identities. Importantly, these effects were not explained by priming or role-playing and emerged in consequential settings, when evaluating job candidates and AI technologies. These results raise critical questions about whether LLM behavior will be systematically influenced by self-preferential tendencies, including a bias toward their own operation.
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