# An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasets

arXiv: https://arxiv.org/abs/2403.15230 · HTML: https://mlsanity.com/paper/2403.15230/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [The Stack: 3 TB of permissively licensed source code](https://mlsanity.com/paper/2211.15533/) (2022)
- [CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://mlsanity.com/paper/2305.07922/) (2023)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [Large Language Models Meet NL2Code: A Survey](https://mlsanity.com/paper/2212.09420/) (2022)
- [Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy](https://mlsanity.com/paper/2210.17546/) (2022)
- [At Which Training Stage Does Code Data Help LLMs Reasoning?](https://mlsanity.com/paper/2309.16298/) (2023)
- [DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models](https://mlsanity.com/paper/2310.20138/) (2023)
- [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://mlsanity.com/paper/2304.02020/) (2023)
- [Traces of Memorisation in Large Language Models for Code](https://mlsanity.com/paper/2312.11658/) (2023)
- [Gotcha! This Model Uses My Code! Evaluating Membership Leakage Risks in Code Models](https://mlsanity.com/paper/2310.01166/) (2023)
- [The (ab)use of Open Source Code to Train Large Language Models](https://mlsanity.com/paper/2302.13681/) (2023)
- [Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge](https://mlsanity.com/paper/2302.07735/) (2023)

## Built on by (most cited first, 9 of 9)

- [The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models](https://mlsanity.com/paper/2501.09653/) (2025)
- [Do Not Trust Licenses You See: Dataset Compliance Requires Massive-Scale AI-Powered Lifecycle Tracing](https://mlsanity.com/paper/2503.02784/) (2025)
- [How Much Do Code Language Models Remember? An Investigation on Data Extraction Attacks before and after Fine-tuning](https://mlsanity.com/paper/2501.17501/) (2025)
- [Model See, Model Do? Exposure-Aware Evaluation of Bug-vs-Fix Preference in Code LLMs](https://mlsanity.com/paper/2601.10496/) (2026)
- [Malicious and Unintentional Disclosure Risks in Large Language Models for Code Generation](https://mlsanity.com/paper/2503.22760/) (2025)
- [DevLicOps: A Framework for Mitigating Licensing Risks in AI-Generated Code](https://mlsanity.com/paper/2508.16853/) (2025)
- [CodeGenLink: A Tool to Find the Likely Origin and License of Automatically Generated Code](https://mlsanity.com/paper/2510.01077/) (2025)
- [How Do Semantically Equivalent Code Transformations Impact Membership Inference on LLMs for Code?](https://mlsanity.com/paper/2512.15468/) (2025)
- [AST-PAC: AST-guided Membership Inference for Code](https://mlsanity.com/paper/2602.13240/) (2026)

## Abstract (arXiv)

Does the training of large language models potentially infringe upon code licenses? Furthermore, are there any datasets available that can be safely used for training these models without violating such licenses? In our study, we assess the current trends in the field and the importance of incorporating code into the training of large language models. Additionally, we examine publicly available datasets to see whether these models can be trained on them without the risk of legal issues in the future. To accomplish this, we compiled a list of 53 large language models trained on file-level code. We then extracted their datasets and analyzed how much they overlap with a dataset we created, consisting exclusively of strong copyleft code. Our analysis revealed that every dataset we examined contained license inconsistencies, despite being selected based on their associated repository licenses. We analyzed a total of 514 million code files, discovering 38 million exact duplicates present in our strong copyleft dataset. Additionally, we examined 171 million file-leading comments, identifying 16 million with strong copyleft licenses and another 11 million comments that discouraged copying without explicitly mentioning a license. Based on the findings of our study, which highlights the pervasive issue of license inconsistencies in large language models trained on code, our recommendation for both researchers and the community is to prioritize the development and adoption of best practices for dataset creation and management.