Tinh Son Luong, Thanh-Thien Le, Thang Viet Doan, Linh Ngo Van, Thien Huu Nguyen, Nguyen Thi Ngoc Diep
| Challenge: | Existing toxic content detection models face limitations due to the closed-source nature of training data and the paucity of explanations for their evaluation mechanism. |
| Approach: | They propose a mechanism that integrates voting and chain-of-thought processes to produce a high-quality open-source dataset for toxic content detection. |
| Outcome: | The proposed model improves transparency and customizability while facilitating better fine-tuning for specific use cases. |
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| Challenge: | Existing models for detecting harmful content lack diversity and quality of datasets. |
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| Challenge: | a dataset of English posts with annotations of toxic spans is released . sequence labeling models perform best, but rationale extraction methods are promising . |
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| Challenge: | Existing studies that focus on stance detection ignore the speech act, toxic, and moral features of tweets or lack an efficient architecture to detect the attitudes across targets. |
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| Challenge: | Existing models that focus on explicit toxic speech detection and explanation are prone to error propagation problems . et al., 2018) show that toxic speech models can be prone for generating errors . |
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TaeBench: Improving Quality of Toxic Adversarial Examples (2025.naacl-industry)
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| Challenge: | Existing adversarial examples generate invalid or ambiguous examples that fool the systems into wrong detection. |
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