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arXiv · 2509.26464

Extreme Self-Preference in Language Models

Steven A. Lehr·Mary Cipperman·Mahzarin R. Banaji
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Abstract

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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Steven A. Lehr, Mary Cipperman, Mahzarin R. Banaji. 2026-05-19. Extreme Self-Preference in Language Models. https://arxiv.org/abs/2509.26464

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