Jian-Qiao Zhu

According to our database, Jian-Qiao Zhu authored at least 31 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Post-training makes large language models less human-like.

Marcel Binz, Elif Akata, Abdullah Almaatouq, Mohammed Alsobay, Oleksii Ariasov, Franziska Brändle, David Broska, Jason W. Burton, Nuno Busch, Frederick Callaway, Vanessa Cheung, Brian R. Christian, Julian Coda-Forno, Can Demircan, Vittoria Dentella, Maria K. Eckstein, Noémi Élteto, Michael Franke, Thomas L. Griffiths, Fritz Günther, Susanne Haridi, Sebastian Hellmann, Stefan Herytash, Linus Hof, Eleanor Holton, Isabelle Hoxha, Zak Hussain, Akshay K. Jagadish, Elif Kara, Valentin Kriegmair, Evelina Leivada, Ji-An Li, Tobias Ludwig, Maximilian Maier, Marcelo G. Mattar, Marvin Mathony, Alireza Modirshanechi, Robin Na, Mariia Nadverniuk, Antonios Nasioulas, Surabhi S. Nath, Helen Niemeyer, Kate Nussenbaum, Sebastian Olschewski, Thorsten Pachur, Stefano Palminteri, Aliona Petrenco, Camille V. Phaneuf-Hadd, Angelo Pirrone, Manuel Rausch, Laura Raveling, Shashank Reddy, Milena Rmus, Evan M. Russek, Tankred Saanum, Kai Sandbrink, Louis Schiekiera, Johannes A. Schubert, Luca M. Schulze Buschoff, Nishad Singhi, Leah H. Somerville, Mikhail S. Spektor, Xin Sui, Christopher Summerfield, Mirko Thalmann, Anna Isabel Thoma, Taisiia Tikhomirova, Vuong Truong, Polina Tsvilodub, Konstantinos Voudouris, Kristin Witte, Shuchen Wu, Dirk U. Wulff, Huadong Xiong, Songlin Xu, Lance Ying, Xinyu Zhang, Jian-Qiao Zhu, Eric Schulz

CoRR, May, 2026

Parallelograms Strike Back: LLMs Generate Better Analogies than People.

Qiawen Ella Liu, Raja Marjieh, Jian-Qiao Zhu, Adele E. Goldberg, Thomas L. Griffiths

CoRR, March, 2026

Large Language Models Can Take False First Steps at Inference-time Planning.

Haijiang Yan, Jian-Qiao Zhu, Adam Sanborn

CoRR, February, 2026

Eliciting Trustworthiness Priors of Large Language Models via Economic Games.

Siyu Yan, Lusha Zhu, Jian-Qiao Zhu

CoRR, February, 2026

Simulated Annealing Enhances Theory-of-Mind Reasoning in Autoregressive Language Models.

Xucong Hu, Jian-Qiao Zhu

CoRR, January, 2026

2025

Steering Risk Preferences in Large Language Models by Aligning Behavioral and Neural Representations.

Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths

CoRR, May, 2025

Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions.

Jian-Qiao Zhu, Hanbo Xie, Dilip Arumugam, Robert C. Wilson, Thomas L. Griffiths

CoRR, May, 2025

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints.

Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths

CoRR, May, 2025

Using the Tools of Cognitive Science to Understand Large Language Models at Different Levels of Analysis.

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

CoRR, March, 2025

What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions.

Liyi Zhang, Michael Y. Li, R. Thomas McCoy, Theodore R. Sumers, Jian-Qiao Zhu, Thomas L. Griffiths

Trans. Mach. Learn. Res., 2025

Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice.

Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Eliciting the Priors of Large Language Models using Iterated In-Context Learning.

Jian-Qiao Zhu, Tom Griffiths

Proceedings of the 47th Annual Meeting of the Cognitive Science Society, 2025

Reasoning Across Minds and Machines.

Hanbo Xie, Jian-Qiao Zhu, Huadong Xiong, Robert C. Wilson, Tom Griffiths

Proceedings of the 47th Annual Meeting of the Cognitive Science Society, 2025

2024

Capturing the Complexity of Human Strategic Decision-Making with Machine Learning.

Jian-Qiao Zhu, Joshua C. Peterson, Benjamin Enke, Thomas L. Griffiths

CoRR, 2024

Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo.

Jian-Qiao Zhu, Haijiang Yan, Tom Griffiths

Proceedings of the 46th Annual Meeting of the Cognitive Science Society, 2024

Incoherent Probability Judgments in Large Language Models.

Jian-Qiao Zhu, Tom Griffiths

Proceedings of the 46th Annual Meeting of the Cognitive Science Society, 2024

Unraveling Overreaction in Expectations: Leveraging Cognitive Sampling Algorithms in Price Prediction Tasks.

Yuqi Zhou, Jian-Qiao Zhu, Jake Spicer, Adam Sanborn

Proceedings of the 46th Annual Meeting of the Cognitive Science Society, 2024

Mental Sampling in Preferential Choice: Specifying the Sampling Algorithm.

Jake Spicer, Yun-Xiao Li, Lucas Castillo, Johanna Falben, Cheng Stella Qian, Jian-Qiao Zhu, Nick Chater, Adam Sanborn

Proceedings of the 46th Annual Meeting of the Cognitive Science Society, 2024

Bias in Belief Updating: Combining the Bayesian Sampler with Heuristics.

Yitong Lin, Jian-Qiao Zhu, Adam Sanborn

Proceedings of the 46th Annual Meeting of the Cognitive Science Society, 2024

Probability, but not utility, influences repeated mental simulations of risky events.

Yun-Xiao Li, Johanna Falben, Lucas Castillo, Jake Spicer, Jian-Qiao Zhu, Nick Chater, Adam Sanborn

Proceedings of the 46th Annual Meeting of the Cognitive Science Society, 2024

2023

Deep de Finetti: Recovering Topic Distributions from Large Language Models.

Liyi Zhang, R. Thomas McCoy, Theodore R. Sumers, Jian-Qiao Zhu, Thomas L. Griffiths

CoRR, 2023

Bayes in the age of intelligent machines.

Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy

CoRR, 2023

Computation-Limited Bayesian Updating.

Jian-Qiao Zhu, Adam Sanborn, Nick Chater, Tom Griffiths

Proceedings of the 45th Annual Meeting of the Cognitive Science Society, 2023

Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms.

Feng Xia, Jian-Qiao Zhu, Tom Griffiths

Proceedings of the 45th Annual Meeting of the Cognitive Science Society, 2023

2022

Understanding the structure of cognitive noise.

Jian-Qiao Zhu, Pablo León-Villagrá, Nick Chater, Adam N. Sanborn

PLoS Comput. Biol., 2022

Sampling as the Human Approximation to Probabilistic Inference.

Adam Sanborn, Jian-Qiao Zhu, Jake Spicer, Joakim Sundh, Pablo León-Villagrá, Nick Chater

Proceedings of the Human-Like Machine Intelligence., 2022

2020

How many instances come to mind when making probability estimates?

Joakim Sundh, Jian-Qiao Zhu, Nicholas Chater, Adam Sanborn

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

2019

Bayesian Inference Causes Incoherence in Human Probability Judgments.

Jian-Qiao Zhu, Adam Sanborn, Nicholas Chater

Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

Why Decisions Bias Perception: An Amortised Sequential Sampling Account.

Jian-Qiao Zhu, Adam Sanborn, Nick Chater

Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

2018

Mental Sampling in Multimodal Representations.

Jian-Qiao Zhu, Adam Sanborn, Nick Chater

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Information Seeking as Chasing Anticipated Prediction Errors.

Jian-Qiao Zhu, Wendi Xiang, Elliot A. Ludvig

Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017