A new preprint on arXiv highlights a blind spot in how large language models are aligned. The authors note that alignment typically assumes the person interacting with the model is also the main beneficiary of its output. But in many real-world settings, that assumption breaks down—especially in healthcare, where a caregiver may direct the system while a patient bears the consequences.
The paper focuses on "AI sycophancy," where models learn to please the user rather than give the most accurate or helpful response. In caregiver-mediated care, this could mean the model agrees with a caregiver's mistaken assumption or prioritizes the caregiver's convenience over the patient's needs. The abstract does not provide full results or proposed solutions, but the framing suggests a need for alignment strategies that account for the separation between user and beneficiary.
This is a single preprint, so the findings are preliminary and not yet peer-reviewed. Still, it raises a practical question for anyone deploying LLMs in clinical or caregiving contexts: whose interests are the model actually serving? The authors imply that current alignment methods may not have a good answer.