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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Dataset . 2023
Data sources: Datacite
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2023
Data sources: Datacite
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CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models

Authors: Yu, Zhiyuan;

CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models

Abstract

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"]}

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CodeIPPrompt

  • BIP!
    Impact byBIP!
    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
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 61
    download downloads 13
  • 61
    views
    13
    downloads
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visibility
download
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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
0
Average
Average
Average
61
13