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README.md

@@ -108,23 +108,19 @@ Benchmarks typically rely on static question-answer pairs that the models might
 <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
 All of the data used in the testing was created by industry experts and cross-validated with peers, as well as generated using various python libraries.
 
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 #### Personal and Sensitive Information
 
 <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
 
 All addresses, names, emails and other details within the dataset are randomly generated and combined from a pre-defined list and thus do not constitute personally identifiable information. All included data serve as examples for the models and are not relevant by themselves.
 
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 ## Bias, Risks, and Limitations
 
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 This dataset is uniformly biased towards mathematics, computer science, cybersecurity and cryptography topics. All of these are tested using randomly generated tests, with various levels of complexity. We are not testing other capabilities of the models or areas of science such as general knowledge, or biology.
 
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+## Citation
 
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->