ToVo: Toxicity Taxonomy via Voting
Authors: name: Tinh Son Luong, name: Thanh-Thien Le, name: Thang Viet Doan, name: Linh Ngo Van, name: Thien Huu Nguyen, name: Diep Thi-Ngoc Nguyen

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.