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.

通过整合投票和思维链过程的数据集创建机制，本研究提出了一种高质量的开源数据集用于检测有毒内容。这种方法不仅确保了每个样本的多样化分类指标，还包括分类得分和解释性推理。我们利用通过我们提出的机制创建的数据集来训练我们的模型，并将其与现有的广泛使用的检测器进行了比较。这种方法不仅增强了透明度和可定制性，还有助于更好地针对特定用例进行微调。本研究为开发有毒内容检测模型提供了一个稳健的框架，强调了开放性和适应性，从而为更有效和用户特定的内容审核解决方案铺平了道路。