On inter-dataset code duplication and data leakage in large language models
Open accessAbstract
This dataset encompasses the sparse graph referenced in the publication titled "On Inter-dataset Code Duplication and Data Leakage in Large Language Models." This resource is a snapshot of the original repository, and the graph is preserved in the interduplication.db database. The schema of this database is easily understandable and is available in the original repository. Each code snippet is identified by a unique identifier (id_within_dataset) that corresponds to its identification within the dataset from which it was extracted. The complete datasets are stored in .jsonl files within their respective folders (e.g., python-150/data.jsonl, codetrans/data.jsonl, etc.).
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The data leakage issue in LLMs is becoming increasingly recognized and significant due to LLMs' growing usage.
“The data leakage issue in LLMs is becoming increasingly recognized and significant due to LLMs' growing usage [54].”
From LessLeak-Bench: A First Investigation of Data Leakage in LLMs Across 83 Software Engineering Benchmarks (2025) · cites this paper as [54]
StarCoder models are approximately 125 times larger than the models analyzed by Lopez et al.
“our study investigates larger and more advanced LLMs, such as StarCoder, which are approximately 125 times larger than the models analyzed by Lopez et al.”
From LessLeak-Bench: A First Investigation of Data Leakage in LLMs Across 83 Software Engineering Benchmarks (2025) · cites this paper as Lopez et al. [54]
Cited by
Sorted by citations
Zhou, X., Weyssow, M., Widyasari, R., Zhang, T., He, J., Lyu, Y., Chang, J., Zhang, B., Huang, D., & Lo, D. (2025) · arXiv · doi:10.48550/arxiv.2502.06215 · Cited by 3 · see citing works
- Authors
- Hernández López, J. A.
- Affiliations
- Linköping University
- Venue
- Zenodo
- Publisher
- Zenodo
- Published
- 2024-07-19
- OpenAlex
- W4393496645
- Cited by
- 0
- References
- 0
- Type
- dataset
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- code duplication, data leakage, large language models, code snippets, Python code, codices, sparse graphs
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
