ToVo: Toxicity Taxonomy via Voting
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929684314324992 |
|---|---|
| author | Luong, Tinh Son Le, Thanh-Thien Doan, Thang Viet Van, Linh Ngo Nguyen, Thien Huu Nguyen, Diep Thi-Ngoc |
| author_facet | Luong, Tinh Son Le, Thanh-Thien Doan, Thang Viet Van, Linh Ngo Nguyen, Thien Huu Nguyen, Diep Thi-Ngoc |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14835 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ToVo: Toxicity Taxonomy via Voting Luong, Tinh Son Le, Thanh-Thien Doan, Thang Viet Van, Linh Ngo Nguyen, Thien Huu Nguyen, Diep Thi-Ngoc Computation and Language Machine Learning 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. |
| title | ToVo: Toxicity Taxonomy via Voting |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2406.14835 |