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Who Is Sycophantic to Whom? Confident LLMs, Human Deference, and the Collapse of Trust Calibration

Deepthika S, Pawni Yadav, B. Sankar · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31579 · cited by 0 works (OpenAlex) · open-access PDF

French

Nous posons la question inverse: quand un LLM est confiant, cohérent et erroné, les utilisateurs se penchent-ils vers lui? Dans une expérience intra-subjects, 32 participants ont appris jusqu'à cinq sujets de science en discutant avec cinq assistants construits sur le même modèle (GPT-5-mini). Inconnu pour les participants, le briefing de chaque assistant avait 0, 25, 50, 75 ou 100% de ses faits de retour remplacés par des fausses fausses plausibles, et chaque assistant a parlé avec la pleine confiance. Un quiz surprise après chaque sujet a offert le fait correct, le faux correct planté, et deux distracteurs, avec une note de confiance pour chaque réponse. Au cours des 104 questionnaires et des 1,040 réponses, les scores ont chuté de 7,4 à 2,9

Machine translation (Meta M2M100); the English original follows.

Abstract

Research on sycophancy asks whether large language models (LLMs) bend towards what their users want to hear. We ask the reverse question: when an LLM is confident, consistent, and wrong, do users bend towards it? In a within-subjects experiment, 32 participants learned up to five science topics by chatting with five assistants built on the same model (GPT-5-mini). Unknown to participants, each assistant's briefing had 0, 25, 50, 75, or 100% of its facts replaced by plausible falsehoods, and every assistant spoke with full confidence. A surprise quiz after each topic offered the correct fact, the planted falsehood, and two distractors, with a confidence rating for every answer. Across 104 quizzes and 1,040 answers, scores fell from 7.4 to 2.9 out of 10, and the planted option was chosen on 60% of falsified items against 11% of correctly taught ones (odds ratio 12.5). Transcript coding showed that whenever the assistant stated a falsehood, it was adopted about 65% of the time at every falsification level. Absorbed falsehoods were held with high confidence (8.4/10), the modeled confidence advantage of right over wrong answers vanished, and the discrimination of confidence for correctness fell from an AUROC of 0.70 to chance, while post-task trust ratings barely moved. Participants tended to return to the chat more when the assistant was worse, but returning went with more absorption, not less, and 30% of all turns pasted quiz questions back to the source being checked. The assistant itself was almost never sycophantic and never conceded under pushback. We introduce a baseline-corrected Human Deference Index to quantify this human-side sycophancy and argue that trustworthiness is a property of the human-AI pair, that sycophancy evaluations should measure both directions, and that learning interfaces must make reliability legible claim by claim.

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Topics: Correctness, Surprise, Flagging, Fell, Ask price, Computer science

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