5 citations · 9 across the 6 of their papers we have counts for
6 papers
A Rational Analysis of the Effects of Sycophantic AI
Rafael M. Batista, Thomas L. Griffiths
People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly…
Cognitive Foundations for Reasoning and Their Manifestation in LLMs
Priyanka Kargupta, Shuyue Stella Li, Haocheng Wang +9
Large language models (LLMs) solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reas…
Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models
Kaiqu Liang, Haimin Hu, Xuandong Zhao +3
Bullshit, as conceptualized by philosopher Harry Frankfurt, refers to statements made without regard to their truth value. While previous work has explored large language model (LL…
VideoGameBench: Can Vision-Language Models complete popular video games?
Alex L. Zhang, Thomas L. Griffiths, Karthik R. Narasimhan +1
Vision-language models (VLMs) have achieved strong results on coding and math benchmarks that are challenging for humans, yet their ability to perform tasks that come naturally to…
An evolutionary perspective on modes of learning in Transformers
Alexander Y. Ku, Thomas L. Griffiths, Stephanie C. Y. Chan
The success of Transformers lies in their ability to improve inference through two complementary strategies: the permanent refinement of model parameters via in-weight learning (IW…
Program-Based Strategy Induction for Reinforcement Learning
Carlos G. Correa, Thomas L. Griffiths, Nathaniel D. Daw
Typical models of learning assume incremental estimation of continuously-varying decision variables like expected rewards. However, this class of models fails to capture more idios…