Recent progress in large language models (LLMs) has led to their widespread adoption in various domains. However, these advancements have also introduced additional safety risks and raised concerns regarding their detrimental impact on already marginalized populations. Despite growing mitigation efforts to develop safety safeguards, such as supervised safety-oriented fine-tuning and leveraging safe reinforcement learning from human feedback, multiple concerns regarding the safety and ingrained biases in these models remain. Furthermore, previous work has demonstrated that models optimized for safety often display exaggerated safety behaviors, such as a tendency to refrain from responding to certain requests as a precautionary measure. As such, a clear trade-off between the helpfulness and safety of these models has been documented in the literature. In this paper, we further investigate the effectiveness of safety measures by evaluating models on already mitigated biases. Using the case of Llama 2 as an example, we illustrate how LLMs' safety responses can still encode harmful assumptions. To do so, we create a set of non-toxic prompts, which we then use to evaluate Llama models. Through our new taxonomy of LLMs responses to users, we observe that the safety/helpfulness trade-offs are more pronounced for certain demographic groups which can lead to quality-of-service harms for marginalized populations.

翻译：大语言模型（LLMs）的最新进展已推动其在多个领域的广泛应用。然而，这些进步也引入了额外的安全风险，并引发了对弱势群体可能遭受负面影响的担忧。尽管通过监督式安全微调、基于人类反馈的安全强化学习等手段，安全防护措施的开发日益增多，但关于模型安全性与固有偏见的诸多问题依然存在。此外，先前研究表明，为安全优化后的模型常表现出过度谨慎的安全行为（例如倾向于以预防性措施拒绝响应某些请求）。文献中已明确记录了这类模型在有用性与安全性之间的权衡。本文以Llama 2为例，通过评估模型对已缓解偏见的处理效果，进一步探究安全措施的有效性。我们创建了一组无毒性提示词来评估Llama模型，并基于对LLM用户响应的新分类体系，发现安全性与有用性的权衡在某些人口统计群体中更为显著，这可能对边缘化人群造成服务质量伤害。