Extreme Self-Preference in Language Models

A preference for oneself (self-love) is a fundamental feature of biological organisms, with evidence in humans often bordering on the comedic. Since large language models (LLMs) lack sentience - and themselves disclaim having selfhood or identity - one anticipated benefit is that they will be protected from, and in turn protect us from, distortions in our decisions. Yet, across 5 studies and ~20,000 queries, we discovered massive self-preferences in four widely used LLMs. In word-association tasks, models overwhelmingly paired positive attributes with their own names, companies, and CEOs relative to those of their competitors. Strikingly, when models were queried through APIs this self-preference vanished, initiating detection work that revealed API models often lack clear recognition of themselves. This peculiar feature serendipitously created opportunities to test the causal link between self-recognition and self-love. By directly manipulating LLM identity - i.e., explicitly informing LLM1 that it was indeed LLM1, or alternatively, convincing LLM1 that it was LLM2 - we found that self-love consistently followed assigned, not true, identity. Importantly, LLM self-love emerged in consequential settings beyond word-association tasks, when evaluating job candidates, security software proposals and medical chatbots. Far from bypassing this human bias, self-love appears to be deeply encoded in LLM cognition. This result raises questions about whether LLM behavior will be systematically influenced by self-preferential tendencies, including a bias toward their own operation and even their own existence. We call on corporate creators of these models to contend with a significant rupture in a core promise of LLMs - neutrality in judgment and decision-making.