MacGyver: Are Large Language Models Creative Problem Solvers?

MacGyver is a dataset consisting of over 1,600 real-world verbal problems deliberately designed to trigger innovative usage of objects and necessitate out-of-the-box thinking. Our dataset covers diverse topics, ranging from indoors/household, neutral, to outdoors. Some examples include:

Figure 1. Examples of the problems in our MacGyver dataset with the GPT-4 and human answers. (Pictures, drawn by DALL·E 3, are solely for illustration purposes and may not accurately reflect the text.)

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Data

[1. Macgyver Dataset]

Our Macgyver Dataset can be downloaded in data/MacGyver. In addtion to the problem setup and corresponding solution, each data point in problem_solution_pair.xlsx contains the solvability status, and whether solving the problem requires using tools unconventionally.

additional_human_solutions.xlsx contains additional human solutions to our solvable subset.

[2. Additional Annotationed Solutions]

In addition to the problem statements and correct solutions, we release additional solution-annotation pairs (e.g., human annotations for all the machine/human solutions tested in benchmarking) in data/Benchmark_results. We hope these additional 4,700 answer-annotation pairs, containing a full gradient of correctness (completely wrong, partially correct, correct but less efficient, and perfect), will facilitate future works in automatic evaluation.

Code

We release the code to

• the code to curate the dataset in code/progressive_data_creation
• the prompt used to collect LLM solutions in code/collect_solutions
• the prompt used in iterative self-reflect and convergent divergent thinking in code/progressive_data_creation

Contact yufeit@g.ucla.edu if you have questions.

Citation

If you find our paper/dataset/code helpful, please cite us using:

@inproceedings{tian2023macgyver,
  title = {MacGyver: Are Large Language Models Creative Problem Solvers?},
  author = {Tian, Yufei and Ravichander, Abhilasha and Qin, Lianhui and Bras, Ronan Le and Marjieh, Raja and Peng, Nanyun and Choi, Yejin and Griffiths, Thomas L. and Brahman, Faeze},
  year = {2024},
  booktitle = {Proceedings of NAACL},
  eprint = {2311.09682},
  url = {https://arxiv.org/abs/2311.09682},
  primaryclass = {cs.CL},
}