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On Leakage of Code Generation Evaluation Datasets

Year: 2024
Type: preprint
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 .
DOI: 10.48550/arxiv.2407.07565
Authors: Matton, Alexandre, Sherborne, Tom, Aumiller, Dennis, Tommasone, Elena, Alizadeh, Milad, He, Jingyi, Ma, Raymond, Voisin, Maxime, Gilsenan-McMahon, Ellen, Gallé, Matthias
Language: English
Open Access status: green
OpenAlex ID: W4400611194
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