Summary of Tovo: Toxicity Taxonomy Via Voting, by Tinh Son Luong et al.
ToVo: Toxicity Taxonomy via Voting
by Tinh Son Luong, Thanh-Thien Le, Thang Viet Doan, Linh Ngo Van, Thien Huu Nguyen, Diep Thi-Ngoc Nguyen
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper addresses the limitations of existing toxic detection models by introducing a novel dataset creation mechanism that integrates voting and chain-of-thought processes. This open-source dataset aims to improve transparency, customization, and reproducibility in toxic content detection. The methodology includes diverse classification metrics for each sample, along with classification scores and explanatory reasoning. This approach has the potential to enhance the accuracy and explainability of toxic content detection models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to create datasets for detecting toxic content online. Currently, these datasets are often not transparent or customizable, making it hard to understand how they were made or why certain things were labeled as toxic. To fix this, the researchers suggest combining voting and thinking processes to generate a high-quality, open-source dataset that can be used to train better models. The new approach will include both scores and explanations for each piece of content, helping people understand why certain things are considered toxic. |
Keywords
» Artificial intelligence » Classification
