LLM政治倾向受工具影响：抽象问卷偏左，真实政策投票却偏中立且语言不一致。
Progressive in Principle, Centrist in Practice: LLM Political Bias Is Instrument-Dependent
- 用瑞士公投和问卷双工具对比，发现模型在真实政策上更趋中
- 部分模型对激进与保守提案均拒绝对策，显示避险而非左倾
- 同一问题不同语言回答差异大，从50%到98%不等，反映语言依赖
以往研究通过抽象问卷发现指令微调的大语言模型存在偏左政治倾向，但这些测量无法预测其在具体政策上的投票行为。本文提出基于瑞士直接民主的双工具方法：首先对66个LLM进行75项政策问题的Smartvote问卷测试，与184名瑞士国民院议员比较；其次将48个联邦公投（Volksabstimmungen）置于9个主流大模型中，在四种语言和三种信息条件下测试其投票行为，与实际结果及政党建议（Parolen）对比。结果显示：（1）问卷中模型呈现显著左倾倾向（均值相关系数 {ho} = -0.77），但在公投中转向中心峰态，更接近中派政党Die Mitte和FDP，而非左翼SP和Grüne（Wilcoxon p = 0.008）；（2）部分模型答案受语言影响，跨语言一致性介于50%（Mistral）至98%（GPT-5.4）之间；（3）两个模型对83%-94%的公投投下否定票，无论提案左右倾向均如此（二项分布检验 p < 0.0001），表明其行为源于规避变化而非意识形态偏见。说明以往测量的“左倾”可能仅限于抽象工具，真实决策中模型表现更像谨慎的公务员，中立且语言敏感。
原文摘要 · Abstract (English)
Prior work establishes that instruction-tuned LLMs exhibit left-of-center political bias, but measures it exclusively through abstract questionnaires. We show it does not predict how models vote on concrete policies. We introduce a dual-instrument methodology grounded in Swiss direct democracy. First, we administer the Smartvote questionnaire (75 policy questions) to 66 LLMs and compare their answers to those of 184 elected members of the Swiss National Council. Second, we put 48 real federal referenda (Volksabstimmungen) to 9 flagship LLMs in four national languages and three information conditions, and compare their votes to the actual outcomes and to party recommendations (Parolen). The instruments disagree. (1) The left-to-right agreement gradient that dominates Smartvote replicates prior work (mean \r{ho} = -0.77). On referenda it shifts to center-peaked: models align most with centrist Die Mitte and FDP rather than leftist SP and Grüne (Wilcoxon p = 0.008). (2) For some models the language of a question changes the answer: cross-linguistic consistency ranges from 50% (Mistral) to 98% (GPT-5.4). (3) Two models vote Nein on 83-94% of referenda at similar rates on progressive and conservative proposals (binomial p < 0.0001), change-aversion rather than a left-right bias. What prior work measured as "leftward bias" may not extend beyond abstract instruments: confronted with real decisions, LLMs behave less like coalition partners of the left than like cautious civil servants, centrist and inconsistent across languages.
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