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🤬 In certain types of tasks, generative models can generate toxic and discriminatory content inspired by historical stereotypes against sensitive attributes (for example, gender, race, and religion). Unfiltered public datasets may also contain inappropriate content, such as pornography, racist images, and social stereotypes, which can contribute to unethical biases in generative models. Furthermore, when prompted with non-English languages, some generative models may perform poorly.
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## License
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The `RewardModel` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.
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🤬 In certain types of tasks, generative models can generate toxic and discriminatory content inspired by historical stereotypes against sensitive attributes (for example, gender, race, and religion). Unfiltered public datasets may also contain inappropriate content, such as pornography, racist images, and social stereotypes, which can contribute to unethical biases in generative models. Furthermore, when prompted with non-English languages, some generative models may perform poorly.
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## Cite as 🤗
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```latex
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@misc{nicholas22aira,
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doi = {10.5281/zenodo.6989727},
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url = {https://github.com/Nkluge-correa/Aira-EXPERT},
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author = {Nicholas Kluge Corrêa and Carolina Del Pino},
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title = {Aira - the AIRES Expert},
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year = {2022},
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publisher = {GitHub},
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journal = {GitHub repository},
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note = {Last updated on 11 May 2023},
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}
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```
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## License
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The `RewardModel` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.
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