Socio-demographic prompting is a commonly employed approach to study cultural
biases in LLMs as well as for aligning models to certain cultures. In this
paper, we systematically probe four LLMs (Llama 3, Mistral v0.2, GPT-3.5 Turbo
and GPT-4) with prompts that are conditioned on culturally sensitive and
non-sensitive cues, on datasets that are supposed to be culturally sensitive
(EtiCor and CALI) or neutral (MMLU and ETHICS). We observe that all models
except GPT-4 show significant variations in their responses on both kinds of
datasets for both kinds of prompts, casting doubt on the robustness of the
culturally-conditioned prompting as a method for eliciting cultural bias in
models or as an alignment strategy. The work also calls rethinking the control
experiment design to tease apart the cultural conditioning of responses from
"placebo effect", i.e., random perturbations of model responses due to
arbitrary tokens in the prompt.

系统研究了四种 LLMs 随不同类型提示文本在不同类型数据集上的回应变化，对于文化条件提示作为检验模型文化偏见或对齐策略的方法的鲁棒性提出了疑问，同时呼吁重新思考控制实验设计以区分回应的文化条件与 ' 安慰剂效应 ' 之间的关系。