arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2501.09653cs.CLcs.AI
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The Heap:一个无污染的多语言代码数据集,用于评估大型语言模型

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

Delft University of Technology(代尔夫特理工大学)

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

AI总结:

The Heap是一个无污染的多语言代码数据集,用于公平评估大型语言模型,覆盖57种编程语言并去重以减少数据清洗需求。

摘要:

近年来,大型语言模型的流行促使了开发大量代码数据集以训练它们。这使得可用于收集和用于下游研究特定行为或评估大型语言模型而不受数据污染影响的代码有限。为了解决这个问题,我们发布了The Heap,这是一个涵盖57种编程语言的大型多语言数据集,已与其他开源代码数据集去重,使研究人员能够进行公平的大型语言模型评估,而无需显著的数据清洗开销。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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