5 papers · 1 filter
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…
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…