Who Says So? Authority Sycophancy in Large Language Models

Large language models (LLMs) may revise their judgments not only in response to content, but also to who endorses a position. We study authority sycophancy, where an LLM changes an already-expressed evaluation after receiving an authority endorsement. We evaluate 10 LLMs across four authority families, two endorsement directions, and two settings with different levels of informational support. Authority-driven stance shift is widespread: mean DSS is positive in 144 of 160 authority conditions and exceeds the matched unspecified-source control in 139. However, the magnitude varies substantially across models and authority families, with no stable source hierarchy. Opposition generally induces stronger shifts than support, and authority-sensitive shift persists when substantive arguments are available. Transition analysis further shows that revisions most often move neutral judgments toward the endorsed position. These results reveal source authority as a substantial but highly heterogeneous influence on LLM judgment.