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This repository contains prompts generated by CodeIPPrompt, a platform used to assess potential intellectual property infringement risks associated with the output of code language models. The source code of the platform can be found at our GitHub repository: https://github.com/zh1yu4nyu/CodeIPPrompt. Detailed information regarding the datasets, as well as usage instructions, can be found in the README.md file. The paper has been accepted by International Conference on Machine Learning (ICML) 2023. If you find this work helpful, please cite us as follows: @inproceedings{yu2023codeipprompt, title={CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models}, author={Yu, Zhiyuan and Wu, Yuhao and Zhang, Ning and Wang, Chenguang and Vorobeychik, Yevgeniy and Xiao, Chaowei}, booktitle={International Conference on Machine Learning}, year={2023}, organization={PMLR} }
{"references": ["Yu, Zhiyuan et al., \"Codeipprompt: Intellectual property infringement assess- ment of code language models,\" in International conference on machine learning, PMLR, 2023"]}
CodeIPPrompt
CodeIPPrompt
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 61 | |
| downloads | 13 |

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