ToVo: Toxicity Taxonomy via Voting
Existing toxic detection models face significant limitations, such as lack of
transparency, customization, and reproducibility. These challenges stem from
the closed-source nature of their training data and the paucity of explanations
for their evaluation mechanism. To address these issues, we propose a dataset
creation mechanism that integrates voting and chain-of-thought processes,
producing a high-quality open-source dataset for toxic content detection. Our
methodology ensures diverse classification metrics for each sample and includes
both classification scores and explanatory reasoning for the classifications.
  We utilize the dataset created through our proposed mechanism to train our
model, which is then compared against existing widely-used detectors. Our
approach not only enhances transparency and customizability but also
facilitates better fine-tuning for specific use cases. This work contributes a
robust framework for developing toxic content detection models, emphasizing
openness and adaptability, thus paving the way for more effective and
user-specific content moderation solutions.
6678dab38a4064c02b75754d
Tinh Son Luong
extracted_pending
2024-06-24T02:32:21.317Z
619550cb4f08530be9bec2cf
/avatars/d1df21b0ff7cfa098d6a0c5375e2c194.svg
Luong Son Tinh
user
convoicon
6678dab38a4064c02b75754e
Thanh-Thien Le
admin_assigned
2024-06-24T14:25:17.287Z
646540145cdb9ab50f85ede3
/avatars/22f077f8ccec9785b8315836dffe1f2a.svg
Thanh-Thien Le
user
thanhthienle
6678dab38a4064c02b75754f
Thang Viet Doan
admin_assigned
2024-06-24T14:25:23.048Z
66614a081761e2aff7bf673c
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Thang Viet Doan
user
babynotta
6678dab38a4064c02b757550
Linh Ngo Van
6678dab38a4064c02b757551
Thien Huu Nguyen
6678dab38a4064c02b757552
Diep Thi-Ngoc Nguyen
ToVo: Toxicity Taxonomy via Voting
Existing toxic detection models face significant limitations, such as lack of
transparency, customization, and reproducibility. These challenges stem from
the closed-source nature of their training data and the paucity of explanations
for their evaluation mechanism. To address these issues, we propose a dataset
creation mechanism that integrates voting and chain-of-thought processes,
producing a high-quality open-source dataset for toxic content detection. Our
methodology ensures diverse classification metrics for each sample and includes
both classification scores and explanatory reasoning for the classifications.
  We utilize the dataset created through our proposed mechanism to train our
model, which is then compared against existing widely-used detectors. Our
approach not only enhances transparency and customizability but also
facilitates better fine-tuning for specific use cases. This work contributes a
robust framework for developing toxic content detection models, emphasizing
openness and adaptability, thus paving the way for more effective and
user-specific content moderation solutions.
A framework for creating an open-source dataset with detailed explanations is proposed to improve transparency and customizability in toxic content detection models.
voting chain-of-thought open-source dataset classification metrics explanatory reasoning fine-tuning