Large language models (LM) generate remarkably fluent text and can be
efficiently adapted across NLP tasks. Measuring and guaranteeing the quality of
generated text in terms of safety is imperative for deploying LMs in the real
world; to this end, prior work often relies on automatic evaluation of LM
toxicity. We critically discuss this approach, evaluate several toxicity
mitigation strategies with respect to both automatic and human evaluation, and
analyze consequences of toxicity mitigation in terms of model bias and LM
quality. We demonstrate that while basic intervention strategies can
effectively optimize previously established automatic metrics on the
RealToxicityPrompts dataset, this comes at the cost of reduced LM coverage for
both texts about, and dialects of, marginalized groups. Additionally, we find
that human raters often disagree with high automatic toxicity scores after
strong toxicity reduction interventions -- highlighting further the nuances
involved in careful evaluation of LM toxicity.

我们探讨了一些减轻大型语言模型毒性的策略，并分析了这些策略对模型偏差和质量的影响。我们发现：虽然基本的干预策略可以有效地优化先前建立的自动指标，但这是以减少有关边缘化团体的文本和方言的语言模型覆盖率的代价。同时，我们还发现，在强减毒干预之后，人类评分员通常不会同意高自动毒性得分 - 这进一步凸显了仔细评估语言模型毒性涉及到的微妙之处。