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Benchmarking Language Model Creativity: A Case Study on Code Generation

This is the official code release accompanying our paper "Benchmarking Language Model Creativity: A Case Study on Code Generation". Our dataset under datasets/CodeForce/NeoCoder contains:

  1. NeoCoder dataset: 199 problems with maximum of 5 temporally relevant constraints.
  2. Historical human solutions: 30 human solutions per problem and their technique detection results (by GPT-4).
  3. Human annotated test cases: Our manually annotated test cases for fixing certain parsing problems from crawling.

Other supporting files: 500 crawled original codeforces problems and crawled raw test cases under datasets/CodeForce/crawled.

File Structure Description

steps/   // callable scripts correspond to each step of denial prompting and creativity evaluation.
src/     // source code of models, evaluators, data collations, etc. 
scripts/ // bash scripts to scale up experiments.

Setup

  1. Setup Zenrows API for scraping: echo "export ZENROWS_API_KEY='yourkey'" >> ~/.bashrc
  2. Setup OpenAI API for generations: echo "export OPENAI_API_KEY='yourkey'" >> ~/.bashrc
  3. Create environment: conda create --name creativity python=3.9
  4. Activate environment: conda activate creativity
  5. Setup environment: pip install -r requirements.txt

Full Steps to Reproduce Our Dataset and Results

If someone wants to use only our NeoCoder dataset to reproduce the results, please run steps Inference and NeoGauge@T Calculation.

Note that the NeoCoder.json file is originally and automatically saved with the name format of {model_name}_diff={diff}_sample={num_sample}_dp={dp_rounds}.json. For simplicity purposes, we manually change the name to NeoCoder to match the dataset name in our paper.

Prepare Dataset

  1. Crawl CodeForce problems: python steps/crawl_codeforce_problem.py --raw-data-dir datasets/CodeForce/raw/CodeForce800spreadsheet.xlsx --save-dir --num-sample --difficulty
  2. Crawl human solutions:python steps/crawl_codeforce_solution.py --crawled-problem-path --save-dir --max-solution-num
  3. Prepare Test Cases: python steps/parse_test_case.py --data-path --output-dir
  4. Manually correcting test cases to match inputs and outputs. We provide our annotated results in datasets/CodeForce/NeoCoder/test_cases_annotated.json

Denial Prompting (Section 3 in the paper)

  1. Generate NeoCoder dataset: python steps/generate_dp.py --problem-set-dir --model-name --num-sample --dp-rounds --output-dir

    In our experiment, we generate NeoCoder by GPT-4 using the following script: bash scripts/generate_dp_dataset.sh

Inference (Equation 1 in the paper)

  1. Inference on NeoCoder dataset: python steps/inference_dp.py --dataset-path --model-name {HF_MODEL_NAME, OPENAI_MODEL_NAME} --dp-rounds --batch-size --output-dir

    We provide a running example in scripts/inference_dp_dataset_llama3.slurm

NeoGauge@T Calculation (Section 4 in the paper)

  1. Evaluate correctness: python steps/creativity_evaluation.py --task correctness --inference-result-path --test-case-path --save-folder --model-family

    We provide a running example in scripts/correctness_evaluation.sh

  2. Detect Techniques: python steps/creativity_evaluation.py --task detection --inference-result-path --human-solution-path

    We provide a running example in scripts/detect_techniques.sh

  3. Final NeoGauge@T Calculation: python steps/creativity_evaluation.py --task creativity --inference-result-path --human-solution-path --save-folder

Citation

If you use this code, please cite the following paper:

@misc{lu2024benchmarkinglanguagemodelcreativity,
      title={Benchmarking Language Model Creativity: A Case Study on Code Generation}, 
      author={Yining Lu and Dixuan Wang and Tianjian Li and Dongwei Jiang and Daniel Khashabi},
      year={2024},
      eprint={2407.09007},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.09007}, 
}