The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models

The recent rise in the popularity of large language models has spurred the development of extensive code datasets needed to train them. This has left limited code available for collection and use in the downstream investigation of specific behaviors, or evaluation of large language models without suffering from data contamination. To address this problem, we release The Heap, a large multilingual dataset covering 57 programming languages that has been deduplicated with respect to other open datasets of code, enabling researchers to conduct fair evaluations of large language models without significant data cleaning overhead.

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Published
2025-04-27
DOI
https://doi.org/10.1109/forge66646.2025.00025
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
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article

The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models

Maliheh Izadi, Jonathan Katzy, Arie van Deursen, Razvan Mihai Popescu
Natural Language Processing Techniques
article

The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models

Maliheh Izadi, Jonathan Katzy, Arie van Deursen, Razvan Mihai Popescu
article en

Abstract

The recent rise in the popularity of large language models has spurred the development of extensive code datasets needed to train them. This has left limited code available for collection and use in the downstream investigation of specific behaviors, or evaluation of large language models without suffering from data contamination. To address this problem, we release The Heap, a large multilingual dataset covering 57 programming languages that has been deduplicated with respect to other open datasets of code, enabling researchers to conduct fair evaluations of large language models without significant data cleaning overhead.

Delft University of Technology (NL)
Openalex Percentile: Top 92%
Natural Language Processing Techniques
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