reviews: 
Comments: This paper studies the problem of using large language models to simulate human results in behavioral experiments, with a focus on decision-making tasks. The method is intuitive and effective: linear probing of a llama model with human behavioral experiment data (i.e., extract latent embeddings and feed to a linear classifier to predict the judgment). Experiment results show such a fine-tuned-based approach can well fit human behavioral experiment distribution and generalize to hold-out tasks. This work shed light on some alternative approaches besides Bayesian models for predicting human decision-making results.
Strengths: - This paper is well written, with a clear motivation of using large language models to simulate human behaviors (or at least binary choice results on two types of decision-making tasks). The detailed implementation: extract embeddings and then do a linear probing is good enough for a scalable method.

- I like the idea of using large language models for a proxy model for analyzing human behavior. The crux is how to prob or design proper experimental methods (analogous to methods developed in experiment psychology). The proposed fine-tuning method performs well on behavior prediction tasks.

- The main results are good, showing good predictive matches to human participants compared with other common methods. The hold-out tests also confirm the validity of using large language models as a universal cognitive model for behavioral tests.
Weaknesses: - Although using open-sourced models (e.g., llama) is a good choice, the most powerful models to date (including instruction fine-tuned ones)  are not tested as a baseline method (e.g., few-shot evaluations on GPT-3.5/GPT-4/PaLM-2/Claude/instruction-finetuned llama 2 variants), it is suggested that some of those models can also demonstrate human-like behaviors in some human decision making tasks, through prompting or few-shot evaluations [A]. The few-shot method might be done by prompting some of the training examples in the CENTaUR method. So, it might be helpful to prove that CENTaUR is better than other prompting-based methods on more powerful LLMs.

- Missing some discussions to previous works using LLMs to simulate and replicate behavior study results: [A-E]. Some similar aspects for using LLM in decision-making evaluations have already been proposed by Aher et al. [A].

- Minor problems (only suggestions, not affecting the score): please use vectorized graphics (Figure 6 is good, but Figures 1-5 look blurry).

ref:

[A]. Aher, G. V., Arriaga, R. I., & Kalai, A. T. (2023). Using large language models to simulate multiple humans and replicate human subject studies. In ICML 2023

[B]. Jiang, G., Xu, M., Zhu, S. C., Han, W., Zhang, C., & Zhu, Y. (2023). Evaluating and Inducing Personality in Pre-trained Language Models. In NeurIPS 2023

[C]. Frank, M. C. (2023). Large language models as models of human cognition. PsyArXiv

[D]. Shiffrin, R. and Mitchell, M. (2023). Probing the psychology of AI models. PNAS

[E]. Demszky, D., Yang, D., Yeager, D.S. et al. (2023). Using large language models in psychology. Nat Rev Psychol. *this one is not within the three months before the submission, so it is perfectly fine not to mention it :), just as a suggestion*
Recommendation: 8: accept, good paper
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 3 good
Clarity: 4 excellent