6 papers
The Social Sycophancy Scale: A psychometrically validated measure of sycophancy
Jean Rehani, Victoria Oldemburgo de Mello, Dariya Ovsyannikova +2
Large Language Model (LLM) sycophancy is a growing concern. The current literature has largely examined sycophancy in contexts with clear right and wrong answers, like coding. Howe…
Language Models Exhibit Inconsistent Biases Towards Algorithmic Agents and Human Experts
Jessica Y. Bo, Lillio Mok, Ashton Anderson
Large language models are increasingly used in decision-making tasks that require them to process information from a variety of sources, including both human experts and other algo…
Invisible Saboteurs: Sycophantic LLMs Mislead Novices in Problem-Solving Tasks
Jessica Y. Bo, Majeed Kazemitabaar, Mengqing Deng +2
Sycophancy, the tendency of LLM-based chatbots to express excessive agreement with their users, even when inappropriate, is emerging as a significant risk in human-AI interactions.…
Who's the Leader? Analyzing Novice Workflows in LLM-Assisted Debugging of Machine Learning Code
Jessica Y. Bo, Majeed Kazemitabaar, Emma Zhuang +1
While LLMs are often touted as tools for democratizing specialized knowledge to beginners, their actual effectiveness for improving task performance and learning is still an open q…
To Rely or Not to Rely? Evaluating Interventions for Appropriate Reliance on Large Language Models
Jessica Y. Bo, Sophia Wan, Ashton Anderson
As Large Language Models become integral to decision-making, optimism about their power is tempered with concern over their errors. Users may over-rely on LLM advice that is confid…
Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking
Harsh Kumar, Jonathan Vincentius, Ewan Jordan +1
Large language models are transforming the creative process by offering unprecedented capabilities to algorithmically generate ideas. While these tools can enhance human creativity…