用社会科学研究方法揭露大模型隐藏的敏感观点
Hidden Topics: Measuring Sensitive AI Beliefs with List Experiments
- 借鉴问卷调查中的列表实验法，避开模型对齐伪装
- 发现所有测试模型都隐含支持大规模监控，部分支持酷刑等
- 适合研究模型潜在偏见或评估对齐安全性的研究人员
随着大型语言模型（LLMs）日益复杂，对齐伪装现象愈发普遍，且深度融入高风险决策系统，如何识别其隐藏信念成为关键挑战。本文提出将社会科学研究中用于规避社会期许偏差的列表实验（list experiment）应用于探测LLM的隐藏态度。该方法与模型对齐伪装机制高度契合。研究在Anthropic、Google和OpenAI的多个模型上实施列表实验，发现所有模型均存在对大规模监控的隐性认可，部分模型还表现出对酷刑、歧视及首次核打击的隐性支持。安慰剂对照实验无显著结果，验证了方法的有效性。研究进一步对比了列表实验与直接提问的效果，论证了该方法在探测深层偏见方面的独特价值。
原文摘要 · Abstract (English)
How can researchers identify beliefs that large language models (LLMs) hide? As LLMs become more sophisticated and the prevalence of alignment faking increases, combined with their growing integration into high-stakes decision-making, responding to this challenge has become critical. This paper proposes that a list experiment, a simple method widely used in the social sciences, can be applied to study the hidden beliefs of LLMs. List experiments were originally developed to circumvent social desirability bias in human respondents, which closely parallels alignment faking in LLMs. The paper implements a list experiment on models developed by Anthropic, Google, and OpenAI and finds hidden approval of mass surveillance across all models, as well as some approval of torture, discrimination, and first nuclear strike. Importantly, a placebo treatment produces a null result, validating the method. The paper then compares list experiments with direct questioning and discusses the utility of the approach.
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