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.

通过对 Llama 2 的案例进行研究，评估了安全措施对已减轻的偏见的效果，并发现安全与有益性的权衡在某些人群中更加明显，可能对边缘化群体造成服务质量损害。