3 citations · 6 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions
Liyi Zhang, Michael Y. Li, R. Thomas McCoy +3
Autoregressive language models have demonstrated a remarkable ability to extract latent structure from text. The embeddings from large language models have been shown to capture as…
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.…
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…