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Exploring ChatGPT for Toxicity Detection in GitHub | PPT
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The document explores the effectiveness of OpenAI
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Keywords {π}
software, pdf, chatgpt, pptx, toxic, toxicity, github, preetha, chatterjee, prompt, search, data, state, free, exploring, detection, comments, developer, tech, world, discussion, dataset, engineering, workflows, download, mulesoft, mcp, model, book, industry, nation, forum, booknet, canada, crack, josanj, cifdaq, safe, report, content, marketing, storytelling, trends, aienhanced, detecting, types, misclassified, annotated, integration, trust,
Topics {βοΈ}
concurrency real-world patterns large-scale data migrations includes personal attacks book industry state tech field fme safe software open source projects software engineering workflows software developer conversation software developer communication automated toxicity detection successful job search model context protocol social media age pptx drrajdeepmadavi mitigating unhealthy interactions identifying toxic behaviors irony nuanced toxicity developer chats improving emotion recognition european business wallet forecasting inflection points manual testing 2024 trend updates hubspot marius sescu teenage engineerings pixeldarts gender shape attitudes open source bleeding-edge build informative code world comprehensive annotated dataset github pull requ github shyamal mishra io/files/icse_nier_24 fine-tuning llms pdf dataset april 2024 preetha marketing report highlight specific types detecting toxic text software engineering leave chatgpt output detecting toxic comments paid search se workflows toxicity detection integrate storytelling novice software software engineers
Questions {β}
- How effective is OpenAI ChatGPT in detecting toxic text on GitHub?
- RQ1: How effective is OpenAI ChatGPT in detecting toxic text on GitHub?
- RQ2: What types of toxic comments are misclassified by ChatGPT?
- What types of toxic comments are misclassified by ChatGPT?
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description:The document explores the effectiveness of OpenAI's ChatGPT in detecting toxic comments on GitHub, analyzing a dataset of 1,597 comments. Results indicate varying performance in identifying toxic behaviors and highlight specific types of toxicity that are often misclassified. It concludes that while ChatGPT is not perfect, it offers a viable method for automated toxicity detection without requiring extensive annotated datasets, with suggestions for further improvements and integration in software engineering workflows.
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