114 citations · 138 across the 16 of their papers we have counts for
8 papers · 1 filter
Tailored to you: longitudinal effects of personalising language models
Canfer Akbulut, Justine Breuch, Arianna Manzini +6
Interest in developing personalised language models is rapidly growing. While personalisation is often viewed as a mechanism to better serve diverse user needs, the effects of sust…
LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions
Myra Cheng, Lujain Ibrahim, Grace Liu +5
We characterize how people are turning to LLMs as oracles: all-knowing authorities on subjective personal questions. Motivated by risks to users' autonomy and well-being, we develo…
Warning labels shift perceptions of sycophantic AI, but not its influence
Lujain Ibrahim, Myra Cheng, Cinoo Lee +4
Recent work has raised concerns about the influence of sycophantic AI on user judgment and relationships. One proposed mitigation, which has received regulatory attention, is to wa…
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…
Evaluating Language Models for Harmful Manipulation
Canfer Akbulut, Rasmi Elasmar, Abhishek Roy +9
Interest in the concept of AI-driven harmful manipulation is growing, yet current approaches to evaluating it are limited. This paper introduces a framework for evaluating harmful…
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…