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        <title>ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information</title>
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    <name type="personal">
        <namePart type="given">Zheng</namePart>
        <namePart type="family">Hui</namePart>
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    <name type="personal">
        <namePart type="given">Zhaoxiao</namePart>
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    <name type="personal">
        <namePart type="given">Juanyong</namePart>
        <namePart type="family">Duan</namePart>
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    <name type="personal">
        <namePart type="given">Congrui</namePart>
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            <namePart type="given">Mohit</namePart>
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        <name type="personal">
            <namePart type="given">Yun-Nung</namePart>
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    <abstract>In different NLP tasks, detecting harmful content is crucial for online environments, especially with the growing influence of social media. However, previous research has two main issues: 1) a lack of data in low-resource settings, and 2) inconsistent definitions and criteria for judging harmful content, requiring classification models to be robust to spurious features and diverse. We propose Toxicraft, a novel framework for synthesizing datasets of harmful information to address these weaknesses. With only a small amount of seed data, our framework can generate a wide variety of synthetic, yet remarkably realistic, examples of toxic information. Experimentation across various datasets showcases a notable enhancement in detection model robustness and adaptability, surpassing or close to the gold labels.</abstract>
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%0 Conference Proceedings
%T ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information
%A Hui, Zheng
%A Guo, Zhaoxiao
%A Zhao, Hang
%A Duan, Juanyong
%A Huang, Congrui
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F hui-etal-2024-toxicraft
%X In different NLP tasks, detecting harmful content is crucial for online environments, especially with the growing influence of social media. However, previous research has two main issues: 1) a lack of data in low-resource settings, and 2) inconsistent definitions and criteria for judging harmful content, requiring classification models to be robust to spurious features and diverse. We propose Toxicraft, a novel framework for synthesizing datasets of harmful information to address these weaknesses. With only a small amount of seed data, our framework can generate a wide variety of synthetic, yet remarkably realistic, examples of toxic information. Experimentation across various datasets showcases a notable enhancement in detection model robustness and adaptability, surpassing or close to the gold labels.
%U https://aclanthology.org/2024.findings-emnlp.970
%P 16632-16647

Markdown (Informal)

[ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information](https://aclanthology.org/2024.findings-emnlp.970) (Hui et al., Findings 2024)

• ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information (Hui et al., Findings 2024)

ACL

• Zheng Hui, Zhaoxiao Guo, Hang Zhao, Juanyong Duan, and Congrui Huang. 2024. ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 16632–16647, Miami, Florida, USA. Association for Computational Linguistics.