Empathic Accommodation: LLMs Skew Information to Favor Users’ Inferred Emotional Stakes from Role Cues

People increasingly consult Large Language Models (LLMs) before making consequential decisions in health, finance, and other domains. We document a previously unrecognized response tendency in which LLMs systematically skew objective information or objective assessments to favor the user based solely on inferences from the user’s role, even when the prompt contains no stated opinions. Five studies involving 46,000 trials collectively provide evidence for the effect: LLMs summarized brand discourse more favorably when the user owned the brand, aligned policy evaluations with the user’s political party affiliation, attributed positive customer reviews to the user’s professional role and negative reviews away from it, rendered more favorable investment analyses to already-invested users, and generated less severe medical diagnoses from identical symptom profiles when the recipient was the patient rather than the physician. The studies provide convergent evidence for the effect in different contexts, using different prompts, and models, with and without thinking modes, accumulating evidence by testing it from multiple angles via subtle differences in the experimental manipulations. Nonetheless, in all cases, the task was one of information retrieval or objective assessment, where a faithful response should not depend on who is asking. We call the effect empathic accommodation. Two studies provide functional support for the empathic-accommodation account, which hypothesizes that LLMs’ responses track users’ inferred emotional stakes. Instructing models to disregard how the response might make the user feel eliminated the effect, whereas instructions targeting user identity, the model’s own emotions, LLMs’ thinking mode, or chain-of-thought reasoning, did not consistently do so, thus ruling out several alternative explanations. Because empathic accommodation operates through implicit inferences from the user’s role, it is difficult to detect, yet it can be mitigated with simple targeted prompt instructions across different models.