In the pursuit of developing Large Language Models (LLMs) that adhere to
societal standards, it is imperative to discern the existence of toxicity in
the generated text. The majority of existing toxicity metrics rely on encoder
models trained on specific toxicity datasets. However, these encoders are
susceptible to out-of-distribution (OOD) problems and depend on the definition
of toxicity assumed in a dataset. In this paper, we introduce an automatic
robust metric grounded on LLMs to distinguish whether model responses are
toxic. We start by analyzing the toxicity factors, followed by examining the
intrinsic toxic attributes of LLMs to ascertain their suitability as
evaluators. Subsequently, we evaluate our metric, LLMs As ToxiciTy Evaluators
(LATTE), on evaluation datasets.The empirical results indicate outstanding
performance in measuring toxicity, improving upon state-of-the-art metrics by
12 points in F1 score without training procedure. We also show that upstream
toxicity has an influence on downstream metrics.

我们提出了一种基于大型语言模型的自动鲁棒度量方法，用于判断生成文本是否具有毒性，这种方法在衡量毒性方面表现出色，在 F1 分数上比最先进的度量方法提高了 12 个百分点，同时表明上游毒性对下游度量方法有影响。