Soft-Label Integration for Robust Toxicity Classification
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929583133032448 |
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| author | Cheng, Zelei Wu, Xian Yu, Jiahao Han, Shuo Cai, Xin-Qiang Xing, Xinyu |
| author_facet | Cheng, Zelei Wu, Xian Yu, Jiahao Han, Shuo Cai, Xin-Qiang Xing, Xinyu |
| contents | Toxicity classification in textual content remains a significant problem. Data with labels from a single annotator fall short of capturing the diversity of human perspectives. Therefore, there is a growing need to incorporate crowdsourced annotations for training an effective toxicity classifier. Additionally, the standard approach to training a classifier using empirical risk minimization (ERM) may fail to address the potential shifts between the training set and testing set due to exploiting spurious correlations. This work introduces a novel bi-level optimization framework that integrates crowdsourced annotations with the soft-labeling technique and optimizes the soft-label weights by Group Distributionally Robust Optimization (GroupDRO) to enhance the robustness against out-of-distribution (OOD) risk. We theoretically prove the convergence of our bi-level optimization algorithm. Experimental results demonstrate that our approach outperforms existing baseline methods in terms of both average and worst-group accuracy, confirming its effectiveness in leveraging crowdsourced annotations to achieve more effective and robust toxicity classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14894 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Soft-Label Integration for Robust Toxicity Classification Cheng, Zelei Wu, Xian Yu, Jiahao Han, Shuo Cai, Xin-Qiang Xing, Xinyu Artificial Intelligence Cryptography and Security Machine Learning Toxicity classification in textual content remains a significant problem. Data with labels from a single annotator fall short of capturing the diversity of human perspectives. Therefore, there is a growing need to incorporate crowdsourced annotations for training an effective toxicity classifier. Additionally, the standard approach to training a classifier using empirical risk minimization (ERM) may fail to address the potential shifts between the training set and testing set due to exploiting spurious correlations. This work introduces a novel bi-level optimization framework that integrates crowdsourced annotations with the soft-labeling technique and optimizes the soft-label weights by Group Distributionally Robust Optimization (GroupDRO) to enhance the robustness against out-of-distribution (OOD) risk. We theoretically prove the convergence of our bi-level optimization algorithm. Experimental results demonstrate that our approach outperforms existing baseline methods in terms of both average and worst-group accuracy, confirming its effectiveness in leveraging crowdsourced annotations to achieve more effective and robust toxicity classification. |
| title | Soft-Label Integration for Robust Toxicity Classification |
| topic | Artificial Intelligence Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2410.14894 |