@inproceedings{matton-etal-2024-leakage,
    title = "On Leakage of Code Generation Evaluation Datasets",
    author = "Matton, Alexandre  and
      Sherborne, Tom  and
      Aumiller, Dennis  and
      Tommasone, Elena  and
      Alizadeh, Milad  and
      He, Jingyi  and
      Ma, Raymond  and
      Voisin, Maxime  and
      Gilsenan-McMahon, Ellen  and
      Gall{\'e}, Matthias",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.findings-emnlp.772/",
    doi = "10.18653/v1/2024.findings-emnlp.772",
    pages = "13215--13223",
    abstract = "In this paper, we consider contamination by code generation test sets, in particular in their use in modern large language models.We discuss three possible sources of such contamination and show findings supporting each of them: (i) direct data leakage, (ii) indirect data leakage through the use of synthetic data and (iii) overfitting to evaluation sets during model selection.To address this, we release Less Basic Python Problems (LBPP): an uncontaminated new benchmark of 161 prompts with their associated Python solutions. LBPP is released at https://huggingface.co/datasets/CohereForAI/lbpp"
}