用大模型模拟欧洲议会投票行为，准确率达79.3%。
Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Models
- 通过角色提示让大模型模拟议员立场，零样本预测投票
- 聚合预测结果在政策立场上与真实群体高度一致
- 方法稳定，适合研究政治极化与政策影响
大语言模型虽能理解或生成政治话语，但普遍存在进步主义左倾偏见。而角色提示已被证明可使模型行为与非对齐的社会经济群体一致。本文探究在仅提供有限信息的情况下，零样本角色提示能否准确预测个体投票行为，并通过聚合预测欧洲议会各党团在多样化政策上的立场。我们评估了预测在反事实论证、不同角色提示及生成方法下的稳定性。结果表明，该方法可合理模拟欧洲议会议员的投票行为，加权F1得分约为0.793。本文构建了2024年欧洲议会议员角色数据集，并开源代码：https://github.com/dess-mannheim/european_parliament_simulation。
原文摘要 · Abstract (English)
Large Language Models (LLMs) display remarkable capabilities to understand or even produce political discourse but have been found to consistently exhibit a progressive left-leaning bias. At the same time, so-called persona or identity prompts have been shown to produce LLM behavior that aligns with socioeconomic groups with which the base model is not aligned. In this work, we analyze whether zero-shot persona prompting with limited information can accurately predict individual voting decisions and, by aggregation, accurately predict the positions of European groups on a diverse set of policies. We evaluate whether predictions are stable in response to counterfactual arguments, different persona prompts, and generation methods. Finally, we find that we can simulate the voting behavior of Members of the European Parliament reasonably well, achieving a weighted F1 score of approximately 0.793. Our persona dataset of politicians in the 2024 European Parliament and our code are available at the following url: https://github.com/dess-mannheim/european_parliament_simulation.
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