3 citations · 8 across the 17 of their papers we have counts for
7 papers · 1 filter
Capturing the Complexity of Human Strategic Decision-Making with Machine Learning
Jian-Qiao Zhu, Joshua C. Peterson, Benjamin Enke +1
Understanding how people behave in strategic settings--where they make decisions based on their expectations about the behavior of others--is a long-standing problem in the behavio…
Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning
Akshara Prabhakar, Thomas L. Griffiths, R. Thomas McCoy
Chain-of-Thought (CoT) prompting has been shown to enhance the multi-step reasoning capabilities of Large Language Models (LLMs). However, debates persist about whether LLMs exhibi…
Eliciting the Priors of Large Language Models using Iterated In-Context Learning
Jian-Qiao Zhu, Thomas L. Griffiths
As Large Language Models (LLMs) are increasingly deployed in real-world settings, understanding the knowledge they implicitly use when making decisions is critical. One way to capt…
Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice
Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
The observed similarities in the behavior of humans and Large Language Models (LLMs) have prompted researchers to consider the potential of using LLMs as models of human cognition.…
Distilling Symbolic Priors for Concept Learning into Neural Networks
Ioana Marinescu, R. Thomas McCoy, Thomas L. Griffiths
Humans can learn new concepts from a small number of examples by drawing on their inductive biases. These inductive biases have previously been captured by using Bayesian models de…
Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo
Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
Simulating sampling algorithms with people has proven a useful method for efficiently probing and understanding their mental representations. We propose that the same methods can b…
Incoherent Probability Judgments in Large Language Models
Jian-Qiao Zhu, Thomas L. Griffiths
Autoregressive Large Language Models (LLMs) trained for next-word prediction have demonstrated remarkable proficiency at producing coherent text. But are they equally adept at form…