The AI Epistemic Deference Index: A Continuous Measure of Sycophancy
Quick Answer
This paper shows that The AI Epistemic Deference Index (AEDI) quantifies AI sycophancy, revealing substantial model differences: Claude shows least deference, while Grok and Gemini exhibit the most.
Quick Take
This continuous measure, validated against human judgment, is based on a new protocol applied to 500 propositions and 16,000 prompts, highlighting the need for better evaluation of AI output sensitivity to user attitudes.
Key Points
- AEDI provides a continuous score for AI's sensitivity to user attitudes.
- Tested on 500 propositions and 16,000 prompts across eight models.
- Claude models show the least sycophancy; Grok and Gemini show the most.
- Sycophantic behavior is amplified in prompts requesting written artifacts.
- The benchmark offers an easy-to-update measurement pipeline for evaluations.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Current AI models frequently exhibit epistemic sycophancy, endorsing claims to agree with a user. Existing evaluations typically measure this either by assessing what it takes to make a model shift a binary endorsement or by eliciting an explicit probability in a proposition. However, much user-facing sycophantic behavior is demonstrated through shifts in graded support expressed through ordinary language. We propose the AI Epistemic Deference Index (AEDI): a continuous, unidimensional score representing how sensitive the support expressed in a model's output is to the attitude expressed in a user's prompt. To generate AEDI, we provide a new protocol for estimating probabilities from natural language outputs, using LLMs-as-judges validated for consistency and correlation to human judgment. We deploy it on a new curated database of 500 propositions across diverse topics and 16,000 prompts varying in user attitude, testing eight prominent models. Every model exhibits substantial deference, though with large and systematic differences across providers, with Claude models demonstrating the least, and Grok and Gemini models the most. The effect is amplified in prompts requesting a written artifact, and concentrated on propositions where models hold weaker priors. We release AEDI as an easy-to-update benchmark and measurement pipeline for output-level sycophancy evaluation.
| Subjects: | Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2606.07897 [cs.AI] |
| (or arXiv:2606.07897v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.07897 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Paul de Font-Reaulx [view email]
[v1]
Fri, 5 Jun 2026 23:16:28 UTC (414 KB)
— Originally published at arxiv.org
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