The fundamental problem in toxicity detection task lies in the fact that the toxicity is ill-defined. This causes us to rely on subjective and vague data in models' training, which results in non-robust and non-accurate results: garbage in - garbage out. This work suggests a new, stress-level-based definition of toxicity designed to be objective and context-aware. On par with it, we also describe possible ways of applying this new definition to dataset creation and model training.

翻译：毒性检测任务的根本问题在于“毒性”这一概念缺乏明确定义。这导致我们在模型训练中依赖主观模糊的数据，从而产生不鲁棒且不准确的结果：垃圾进，垃圾出。本研究提出一种基于应激水平的新型毒性定义，旨在实现客观性与上下文感知特性。同时，我们阐述了将该新定义应用于数据集构建与模型训练的可行路径。