文化条件作用还是安慰剂？论社会人口学提示的有效性
Cultural Conditioning or Placebo? On the Effectiveness of Socio-Demographic Prompting
AI总结：
本研究通过在文化敏感与中性数据集上测试四种大语言模型对不同提示的响应，发现多数模型响应波动显著，质疑了文化条件提示方法的稳健性，呼吁优化对照实验以区分文化作用与安慰剂效应。
摘要：
社会人口学提示是研究大语言模型（LLM）文化偏见以及让模型适配特定文化的常用方法。本文系统探究了四种LLM——Llama 3、Mistral v0.2、GPT-3.5 Turbo和GPT-4，使用带有文化敏感与非敏感线索的提示，在被认为具有文化敏感性的数据集（EtiCor和CALI）以及中性数据集（MMLU和ETHICS）上开展实验。我们观察到，除GPT-4外，所有模型在两类数据集上对两类提示的响应都呈现出显著差异，这让人们对文化条件提示作为诱发模型文化偏见的方法或对齐策略的稳健性产生了质疑。本研究还呼吁重新思考对照实验设计，以区分响应的文化条件作用与“安慰剂效应”，即由提示中任意token导致的模型响应随机扰动。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
