This repository contains the code for the paper "Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?", presented at ICML 2024.

To install the required packages, run:

pip install -r requirements.txt

• Generate Uninstantiated World Models
  Run generate_linear_world_model.py to generate empty linear world models. generate_consistency_model.py generates pairs which differ only in consistency. generate_dual_models.py generates a comparison problem and a transfer problem both implying the same arithmetic computation.

• Instantiate and Convert Problems to Natural Language
  Run generate_data.py to generate natural language word problem data sets from empty world models. generate_comp.py and generate_trans.py can be used to generate pairs of problems which share the same variable instantiations for comparison vs transfer experiments. generate_carry.py and generate_nocarry.py can be used to generate pairs of problems which are identical up to differences in variable quantities, where the problems generated by the former always contain carries.

Run python eval.py to load a model, evaluate it, and store its preditions. The arguments are handeld using Hydra:

• test_type indicates for which of the three biases considered in the paper the model should be test for (consistency, comparison_vs_transfer, or carry)
• model the HuggingFace identifier of the model that should be tested
• solution_mode indicates how teh model should be prompted (direct or cot)
• data_path is the path to .csv file containing the problems generated for the corresponding test_type
• hf_token_path is the path to a .txt file containing a token to access gated HuggingFace models (e.g., LLaMA2)

The default configuration can be found in conf/config_eval.yaml. The script stores the predictions in eval_out/[test_type]/[solution_mode]/[model_id] and uploads the metrics on wandb. You can disable wandb sync by setting wandb_mode=offline.

Please cite as:

@inproceedings{opedal2024language,
  title = {Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?},
  author = {Opedal, Andreas and Stolfo, Alessandro and Shirakami, Haruki and Jiao, Ying and Cotterell, Ryan and Schölkopf, Bernhard and Saparov, Abulhair and Sachan, Mrinmaya},
  booktitle = {Forty-first International Conference on Machine Learning},
  month = july,
  year = {2024},
  url = {https://arxiv.org/abs/2401.18070},
}