Jian-Qiao Zhu

35 papers A* 2B 14Journal 18
YearRankTypeTitle / Venue / Authors
2026 J jnl
Eliciting Trustworthiness Priors of Large Language Models via Economic Games.
CoRR
Siyu Yan, Lusha Zhu, Jian-Qiao Zhu
2026 J jnl
Large Language Models Can Take False First Steps at Inference-time Planning.
CoRR
Haijiang Yan, Jian-Qiao Zhu, Adam Sanborn
2026 J jnl
Parallelograms Strike Back: LLMs Generate Better Analogies than People.
CoRR
Qiawen Ella Liu, Raja Marjieh, Jian-Qiao Zhu, Adele E. Goldberg, Thomas L. Griffiths
2026 J jnl
Simulated Annealing Enhances Theory-of-Mind Reasoning in Autoregressive Language Models.
CoRR
Xucong Hu, Jian-Qiao Zhu
2025 B conf
Eliciting the Priors of Large Language Models using Iterated In-Context Learning.
CogSci
Jian-Qiao Zhu, Tom Griffiths
2025 A* conf
Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice.
ICLR
Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
2025 B conf
Reasoning Across Minds and Machines.
CogSci
Hanbo Xie, Jian-Qiao Zhu, Huadong Xiong, Robert C. Wilson, Tom Griffiths
2025 J jnl
Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints.
CoRR
Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
2025 J jnl
Steering Risk Preferences in Large Language Models by Aligning Behavioral and Neural Representations.
CoRR
Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
2025 J jnl
Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions.
CoRR
Jian-Qiao Zhu, Hanbo Xie, Dilip Arumugam, Robert C. Wilson, Thomas L. Griffiths
2025 J jnl
Using the Tools of Cognitive Science to Understand Large Language Models at Different Levels of Analysis.
CoRR
Alexander Ku, Declan Campbell, Xuechunzi Bai, Jiayi Geng, Ryan Liu, Raja Marjieh, R. Thomas McCoy, Andrew Nam, Ilia Sucholutsky, Veniamin Veselovsky, Liyi Zhang, Jian-Qiao Zhu, Thomas L. Griffiths
2025 J jnl
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions.
Trans. Mach. Learn. Res.
Liyi Zhang, Michael Y. Li, R. Thomas McCoy, Theodore R. Sumers, Jian-Qiao Zhu, Thomas L. Griffiths
2024 B conf
Bias in Belief Updating: Combining the Bayesian Sampler with Heuristics.
CogSci
Yitong Lin, Jian-Qiao Zhu, Adam Sanborn
2024 J jnl
Capturing the Complexity of Human Strategic Decision-Making with Machine Learning.
CoRR
Jian-Qiao Zhu, Joshua C. Peterson, Benjamin Enke, Thomas L. Griffiths
2024 J jnl
Eliciting the Priors of Large Language Models using Iterated In-Context Learning.
CoRR
Jian-Qiao Zhu, Thomas L. Griffiths
2024 B conf
Incoherent Probability Judgments in Large Language Models.
CogSci
Jian-Qiao Zhu, Tom Griffiths
2024 J jnl
Incoherent Probability Judgments in Large Language Models.
CoRR
Jian-Qiao Zhu, Thomas L. Griffiths
2024 J jnl
Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice.
CoRR
Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
2024 B conf
Mental Sampling in Preferential Choice: Specifying the Sampling Algorithm.
CogSci
Jake Spicer, Yun-Xiao Li, Lucas Castillo, Johanna Falben, Cheng Stella Qian, Jian-Qiao Zhu, Nick Chater, Adam Sanborn
2024 B conf
Probability, but not utility, influences repeated mental simulations of risky events.
CogSci
Yun-Xiao Li, Johanna Falben, Lucas Castillo, Jake Spicer, Jian-Qiao Zhu, Nick Chater, Adam Sanborn
2024 B conf
Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo.
CogSci
Jian-Qiao Zhu, Haijiang Yan, Tom Griffiths
2024 J jnl
Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo.
CoRR
Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
2024 B conf
Unraveling Overreaction in Expectations: Leveraging Cognitive Sampling Algorithms in Price Prediction Tasks.
CogSci
Yuqi Zhou, Jian-Qiao Zhu, Jake Spicer, Adam Sanborn
2023 J jnl
Bayes in the age of intelligent machines.
CoRR
Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy
2023 B conf
Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms.
CogSci
Feng Xia, Jian-Qiao Zhu, Tom Griffiths
2023 B conf
Computation-Limited Bayesian Updating.
CogSci
Jian-Qiao Zhu, Adam Sanborn, Nick Chater, Tom Griffiths
2023 J jnl
Deep de Finetti: Recovering Topic Distributions from Large Language Models.
CoRR
Liyi Zhang, R. Thomas McCoy, Theodore R. Sumers, Jian-Qiao Zhu, Thomas L. Griffiths
2022 — ch.
Sampling as the Human Approximation to Probabilistic Inference.
Human-Like Machine Intelligence
Adam Sanborn, Jian-Qiao Zhu, Jake Spicer, Joakim Sundh, Pablo León-Villagrá, Nick Chater
2022 J jnl
Understanding the structure of cognitive noise.
PLoS Comput. Biol.
Jian-Qiao Zhu, Pablo León-Villagrá, Nick Chater, Adam N. Sanborn
2020 B conf
How many instances come to mind when making probability estimates?
CogSci
Joakim Sundh, Jian-Qiao Zhu, Nicholas Chater, Adam Sanborn
2019 B conf
Bayesian Inference Causes Incoherence in Human Probability Judgments.
CogSci
Jian-Qiao Zhu, Adam Sanborn, Nicholas Chater
2019 B conf
Why Decisions Bias Perception: An Amortised Sequential Sampling Account.
CogSci
Jian-Qiao Zhu, Adam Sanborn, Nick Chater
2018 A* conf
Mental Sampling in Multimodal Representations.
NeurIPS
Jian-Qiao Zhu, Adam Sanborn, Nick Chater
2017 B conf
Information Seeking as Chasing Anticipated Prediction Errors.
CogSci
Jian-Qiao Zhu, Wendi Xiang, Elliot A. Ludvig
2017 J jnl
Mental Sampling in Multimodal Representations.
CoRR
Jian-Qiao Zhu, Adam N. Sanborn, Nick Chater