1 citations · 3 across the 9 of their papers we have counts for
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Accumulating Context Changes the Beliefs of Language Models
Jiayi Geng, Howard Chen, Ryan Liu +4
Language model (LM) assistants are increasingly used in applications such as brainstorming and research. Improvements in memory and context size have allowed these models to become…
Are Large Language Models Sensitive to the Motives Behind Communication?
Addison J. Wu, Ryan Liu, Kerem Oktar +2
Human communication is motivated: people speak, write, and create content with a particular communicative intent in mind. As a result, information that large language models (LLMs)…
Evaluating Language Models' Evaluations of Games
Katherine M. Collins, Cedegao E. Zhang, Graham Todd +9
Reasoning is not just about solving problems -- it is also about evaluating which problems are worth solving at all. Evaluations of artificial intelligence (AI) systems primarily f…
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…
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…
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
Ryan Liu, Theodore R. Sumers, Ishita Dasgupta +1
In day-to-day communication, people often approximate the truth - for example, rounding the time or omitting details - in order to be maximally helpful to the listener. How do larg…