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What are you reporting:

• Evaluation dataset(s) found in a pre-training corpus. (e.g. COPA found in ThePile)
• Evaluation dataset(s) found in a pre-trained model. (e.g. FLAN T5 has been trained on ANLI)

Contaminated Evaluation Dataset(s):

• openai_humaneval
• mbpp

Contaminated Corpora:

• EleutherAI/pile
• bigcode/the-stack

Approach:

• Data-based approach
• Model-based approach

Description of your method, 3-4 sentences. Evidence of data contamination:

An example in the test data (i.e., those of MBPP or HumanEval), is noted as contaminated if the aggregated similarity score is 100, i.e., a perfect match exists on the surface- or semantic-level. Levenshtein similarity score is used to measure surface-level similarity between programs and Dolos toolkit), which is a source code plagiarism detection tool for education purposes, measure semantic similarity between programs.

Note: False positives do exist despite 100% match due to either the example being too simple and obvious, or being flagged as being similar to the gold program despite being quite different.

Citation

Is there a paper that reports the data contamination or describes the method used to detect data contamination? Yes

url: https://arxiv.org/abs/2403.04811

  title={Quantifying contamination in evaluating code generation capabilities of language models},
  author={Riddell, Martin and Ni, Ansong and Cohan, Arman},
  journal={arXiv preprint arXiv:2403.04811},
  year={2024}
}

Important! If you wish to be listed as an author in the final report, please complete this information for all the authors of this Pull Request.

Full name: Ameya Prabhu
Institution: Tübingen AI Center, University of Tübingen
Email: ameya@prabhu.be

Thank you for your contribution!

Best,
Oscar