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SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safety.

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When POS data sets don't add up: Combatting sample bias., , and . LREC, page 4472-4475. European Language Resources Association (ELRA), (2014)Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science, , , , and . Minneapolis, Minnesota, Association for Computational Linguistics, (June 2019)How to Use Large Language Models for Text Coding: The Case of Fatherhood Roles in Public Policy Documents., , , , and . CoRR, (2023)The State of Profanity Obfuscation in Natural Language Processing., and . CoRR, (2022)Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview., , and . CoRR, (2019)Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers., , , , and . CoRR, (2022)Leveraging Social Interactions to Detect Misinformation on Social Media., , , and . CoRR, (2023)Experiments with crowdsourced re-annotation of a POS tagging data set., , and . ACL (2), page 377-382. The Association for Computer Linguistics, (2014)Demographic Factors Improve Classification Performance.. ACL (1), page 752-762. The Association for Computer Linguistics, (2015)Hard and Soft Evaluation of NLP models with BOOtSTrap SAmpling - BooStSa., , , and . ACL (demo), page 127-134. Association for Computational Linguistics, (2022)