ToVo: Toxicity Taxonomy via Voting

TL;DR

This paper introduces ToVo, a new open-source dataset and framework for toxic content detection that emphasizes transparency, customization, and reproducibility through voting and chain-of-thought processes.

Contribution

It presents a novel dataset creation mechanism combining voting and reasoning, and demonstrates improved transparency and adaptability in toxic content detection models.

Findings

Enhanced transparency and explainability in toxicity detection.

Improved model customization for specific use cases.

Open-source dataset facilitates reproducibility and further research.

Abstract

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.