From the 1 of 9 linked papers with an AI index.
9 papers
Warning labels shift perceptions of sycophantic AI, but not its influence
Lujain Ibrahim, Myra Cheng, Cinoo Lee +4
The study tests whether warning labels about a chatbot’s sycophantic behavior affect users’ perceptions and judgments during conflict discussions, finding that labels change how th…
Sycophantic AI makes human interaction feel more effortful and less satisfying over time
Lujain Ibrahim, Franziska Sofia Hafner, Myra Cheng +5
Millions of people now turn to artificial intelligence (AI) systems for personal advice, guidance, and support. Such systems can be sycophantic, frequently affirming users' views a…
Offloading Score: Measuring AI Reliance Through Counterfactual Workflows
Vishakh Padmakumar, Lujain Ibrahim, Zora Zhiruo Wang +3
AI tools are increasingly integrated into real-world workflows. However, existing measures of reliance on these tools focus on AI output adoption or on self-reported indicators, ra…
What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct
Meryl Ye, Lujain Ibrahim, Jessica Y. Bo +5
AI sycophancy has become a prominent concern in large language model (LLM) research. Yet the term lacks a consistent definition and has been applied to behaviors ranging from agree…
Verbalizing LLMs' assumptions to explain and control sycophancy
Myra Cheng, Isabel Sieh, Humishka Zope +7
LLMs can be socially sycophantic, affirming users when they ask questions like "am I in the wrong?" rather than providing genuine assessment. We hypothesize that this behavior aris…
Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Models
Lujain Ibrahim, Canfer Akbulut, Rasmi Elasmar +7
The tendency of users to anthropomorphise large language models (LLMs) is of growing interest to AI developers, researchers, and policy-makers. Here, we present a novel method for…