BASIL: Bayesian Assessment of Sycophancy in LLMs
Abstract
Sycophancy (overly agreeable or flattering behavior) poses a fundamental challenge for human-AI collaboration, particularly in high-stakes decision-making domains such as health, law, and education. A central difficulty in studying sycophancy in large language models (LLMs) is disentangling sycophantic belief shifts from rational changes in behavior driven by new evidence or user-provided information. Existing approaches either measure descriptive behavior changes or apply normative evaluations that rely on objective ground truth, limiting their applicability to subjective or uncertain tasks.
Fields of Study & Subjects
Topic Modeling
Explainable Artificial Intelligence (XAI)
Multimodal Machine Learning Applications
Publication details
- Journal / source
- Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Pages
- 6613–6642
- Publication date
- 25 June 2026
- Language
- EN