The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 16 Jan 2025.
Builds on
11 of 19 references.
References · 11
- 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
- 2023StarCoder: may the source be with you!cited by 790
- 2024StarCoder 2 and The Stack v2: The Next Generationcited by 484
- 2021Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpuscited by 481
- 2022The Stack: 3 TB of permissively licensed source codecited by 354
- 2024CodeGemma: Open Code Models Based on Gemmacited by 145
- 2018The Adverse Effects of Code Duplication in Machine Learning Models of Codecited by 123
- 2024LLM Dataset Inference: Did you train on my dataset?cited by 107
- 2021An Interpretability Illusion for BERTcited by 94
- 2023The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AIcited by 73
- 2024An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasetscited by 8
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Citing papers per year: 2026: 4.
4 papers.
- 2026CMind: An AI Agent for Localizing C Memory Bugscited by 0
- 2026A Taxonomy of Programming Languages for Code Generationcited by 0
- 2026Automated Attention Pattern Discovery at Scale in Large Language Modelscited by 0
- 2026Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Taskcited by 0
- 2026Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Task18 Aug 2026
- 2026Automated Attention Pattern Discovery at Scale in Large Language Models4 Apr 2026
- 2026A Taxonomy of Programming Languages for Code Generation31 Mar 2026
- 2026CMind: An AI Agent for Localizing C Memory Bugs20 Jan 2026