LifeTox：揭示生活建议中的隐性毒性
LifeTox: Unveiling Implicit Toxicity in Life Advice
AI总结：
针对生活建议场景中的隐性毒性检测，构建LifeTox数据集，微调RoBERTa达到或超越大模型零样本性能，并开源数据集及三种规模模型。
摘要：
随着大型语言模型日益融入日常生活，检测不同语境下的隐性毒性至关重要。为此，我们引入了LifeTox，一个旨在识别广泛寻求建议场景中隐性毒性的数据集。与现有的安全数据集不同，LifeTox通过开放式问题从个人经历中衍生出多样化的语境。实验表明，在LifeTox上微调的RoBERTa在毒性分类任务中达到或超越了大型语言模型的零样本性能。这些结果凸显了LifeTox在应对隐性毒性固有复杂挑战方面的有效性。我们开源了该数据集（https://huggingface.co/datasets/mbkim/LifeTox）以及LifeTox审核员系列模型；350M、7B和13B。
英文摘要：
As large language models become increasingly integrated into daily life, detecting implicit toxicity across diverse contexts is crucial. To this end, we introduce LifeTox, a dataset designed for identifying implicit toxicity within a broad range of advice-seeking scenarios. Unlike existing safety datasets, LifeTox comprises diverse contexts derived from personal experiences through open-ended questions. Experiments demonstrate that RoBERTa fine-tuned on LifeTox matches or surpasses the zero-shot performance of large language models in toxicity classification tasks. These results underscore the efficacy of LifeTox in addressing the complex challenges inherent in implicit toxicity. We open-sourced the dataset\footnote{\url{https://huggingface.co/datasets/mbkim/LifeTox}} and the LifeTox moderator family; 350M, 7B, and 13B.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
