114 citations · 129 across the 6 of their papers we have counts for
10 papers
Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively
Li Lucy, Myra Cheng, Svenja Guhr +7
The sycophancy of language models, or their failure to push back against faulty user input, is now widely studied. However, if LMs do push back, is sycophancy solved? We show that…
Mitigating Social Sycophancy via Pluralistic Preference Optimization
Stephane Hatgis-Kessell, Myra Cheng, Xiaoxuan Hou +4
Personal advice, including relationship advice, now ranks among the most common uses of generative AI. But language models (LMs) exhibit sycophancy: they affirm users much more oft…
Cultural Compass: A Framework for Organizing Societal Norms to Detect Violations in Human-AI Conversations
Myra Cheng, Vinodkumar Prabhakaran, Alice Oh +5
Generative AI models ought to be useful and safe across cross-cultural contexts. One critical step toward this goal is understanding how AI models adhere to sociocultural norms. Wh…
Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs
Myra Cheng, Robert D. Hawkins, Dan Jurafsky
Large language models (LLMs) frequently fail to challenge users' harmful beliefs in domains ranging from medical advice to social reasoning. We argue that these failures can be und…
Generation Space Size: Understanding and Calibrating Open-Endedness of LLM Generations
Sunny Yu, Ahmad Jabbar, Robert Hawkins +2
Different open-ended generation tasks require different degrees of output diversity. However, current LLMs are often miscalibrated. They collapse to overly homogeneous outputs for…
Attention to Non-Adopters
Kaitlyn Zhou, Kristina Gligorić, Myra Cheng +7
Although language model-based chat systems are increasingly used in daily life, most Americans remain non-adopters of chat-based LLMs -- as of June 2025, 66% had never used ChatGPT…
Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
Myra Cheng, Cinoo Lee, Pranav Khadpe +3
Both the general public and academic communities have raised concerns about sycophancy, the phenomenon of artificial intelligence (AI) excessively agreeing with or flattering users…
ELEPHANT: Measuring and understanding social sycophancy in LLMs
Myra Cheng, Sunny Yu, Cinoo Lee +3
LLMs are known to exhibit sycophancy: agreeing with and flattering users, even at the cost of correctness. Prior work measures sycophancy only as direct agreement with users' expli…
HumT DumT: Measuring and controlling human-like language in LLMs
Myra Cheng, Sunny Yu, Dan Jurafsky
Should LLMs generate language that makes them seem human? Human-like language might improve user experience, but might also lead to deception, overreliance, and stereotyping. Asses…
Thinking beyond the anthropomorphic paradigm benefits LLM research
Lujain Ibrahim, Myra Cheng
Anthropomorphism, or the attribution of human traits to technology, is an automatic and unconscious response that occurs even in those with advanced technical expertise. In this po…