Kati8/ICLR_2023_2025 huggingface dataset:
authors: Marcel Binz
authors: Eric Schulz
reviews: 
Comments: This work presents a novel cognitive model that uses embeddings from an LLM (LLaMa), together with a regression model, to predict human decision-making behavior. The model outperforms both the pre-trained LLM and domain-specific cognitive models, accounts for individual differences, and can generalize to a holdout task.
Strengths: - This work presents an interesting and novel approach to cognitive modeling that outperforms domain-specific cognitive models.
- The model is shown to be capable of generating qualitative cognitive insights, in addition to superior quantitative performance.
- The model accounts for individual differences.
- The model generalizes to a novel task.
- The paper includes some interesting discussion of the broader implications of this approach for cognitive science.
Weaknesses: - I am not sure if this is a weakness per se, but the work is primarily oriented toward cognitive science. It may be better suited to a more cog-sci oriented venue. However, I think the work generally makes a strong contribution and would be happy for it be published at ICLR.
- My primary substantive concern is that the model is only evaluated on publicly available datasets. Do the authors know whether this data is included in LLaMa's pretraining data? I'm not entirely certain, but given the open-source nature of the model I think it should be possible to determine this. This seems like an important factor for considering how generalizable the approach will be to new datasets.
- For the holdout task, is there an appropriate domain-specific model with which to compare CENTaUR?
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
reviews: 
Comments: The authors propose to fine-tune large language models (LLM) on real human decision making behavior / tasks from psychology to potentially better align them with people and provide better predictions of behavior. They find that fine-tuning is largely successful on two such tasks and allow for at least some generalization to a third hold out task.
Strengths: I found this paper really interesting and worthwhile. While I suspect that there is much more work to be done in assessing the results, the main result is clear and fascinating: LLMs can quickly adapt to predict specific human behaviors. Perhaps most interesting is that this only requires linear regression. While this isn't really fine-tuning in the typical sense and could be considered a limitation (the authors could have trained a single layer or so, but with LLMs this is not easy by any means), I find it useful to know that only a simple transformation is needed. In fact, one could argue that the model didn't really have to learn anything about people. Everything the model needs appears to be located in the final input representation. One could further argue that this implies that LLMs already know how to emulate a diverse range of behavior, but that prompting is not the best way to test for it. In that sense, I think the primary contribution is strong. I don't think the authors needed to name their linear model, as the contribution is empirical and not methodological in some sense, but that's not a weakness given the aim of the paper.
Weaknesses: The main weakness in my view is the difficulty in comparing to past work in the relevant domains and which are cited in the work. Past work appears to use different metrics, different splits of the data, and different baseline models. For example, BEAST does not appear to be the best or only relevant baseline, which is also usually evaluated using mean squared error. There is also a history of work behind the choices13k dataset with machine learning methods that the authors don't review. The authors also don't provide more interpretable metrics such as accuracy. One option to fix this would be make better comparisons using a larger set of relevant models, while another could be to focus on the success of fine-tuning more generally, which is already interesting, and less on benchmarks.

More generally, the claims of the paper could be toned down a bit. The space of human behavior is massive, and success in fine-tuning to extremely narrow ranges of behavior does not guarantee any particular success rate with other kinds. LLMs already fall short in matching human intelligence in some respects, and thus can't mimic such behavior. This may even be true or more nuanced "non-intelligent" behavior as well.
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: 3 good