134 citations · 242 across the 39 of their papers we have counts for
6 papers · 1 filter
Failing to Falsify: Evaluating and Mitigating Confirmation Bias in Language Models
Ayush Rajesh Jhaveri, Anthony GX-Chen, Ilia Sucholutsky +1
Confirmation bias, the tendency to seek evidence that supports rather than challenges one's belief, hinders one's reasoning ability. We examine whether large language models (LLMs)…
Under the Influence: Quantifying Persuasion and Vigilance in Large Language Models
Sasha Robinson, Katherine M. Collins, Ilia Sucholutsky +1
With increasing integration of Large Language Models (LLMs) into areas of high-stakes human decision-making, it is important to understand the risks they introduce as advisors. To…
Levels of Analysis for Large Language Models
Alexander Y. Ku, Declan Campbell, Xuechunzi Bai +10
Modern artificial intelligence systems, such as large language models, are increasingly powerful but also increasingly hard to understand. Recognizing this problem as analogous to…
What is a Number, That a Large Language Model May Know It?
Raja Marjieh, Veniamin Veselovsky, Thomas L. Griffiths +1
Numbers are a basic part of how humans represent and describe the world around them. As a consequence, learning effective representations of numbers is critical for the success of…
Characterizing Similarities and Divergences in Conversational Tones in Humans and LLMs by Sampling with People
Dun-Ming Huang, Pol Van Rijn, Ilia Sucholutsky +2
Conversational tones -- the manners and attitudes in which speakers communicate -- are essential to effective communication. Amidst the increasing popularization of Large Language…
Large Language Models Assume People are More Rational than We Really are
Ryan Liu, Jiayi Geng, Joshua C. Peterson +2
In order for AI systems to communicate effectively with people, they must understand how we make decisions. However, people's decisions are not always rational, so the implicit int…