多样性中的统一？基于大型语言模型（LLM）的2024年欧洲议会选举预测中的语境偏差
United in Diversity? Contextual Biases in LLM-Based Predictions of the 2024 European Parliament Elections
AI总结：
本研究以2024年欧洲议会选举预测为场景，探究基于LLM的合成样本在民意预测中的语境依赖偏差，发现其预测准确性在不同国家和语言语境分布不均且整体失败，强调其适用性有限并为缓解LLM应用不平等提供参考。
摘要：
基于大型语言模型（LLM）的“合成样本”被认为可作为人类调查的高效替代方案，前提是其训练数据包含人类态度和行为的信息。然而，LLM合成样本可能存在偏差，例如由于训练数据和微调过程无法代表多样语境。此类偏差可能加剧研究、政策制定和社会中的现有偏差。因此，研究人员需调查LLM生成的合成样本是否及在何种条件下可用于民意预测。本研究通过预测2024年欧洲议会选举结果，考察基于LLM的个体民意预测在多大程度上存在语境依赖偏差。我们向三个LLM提供26000名符合条件的欧洲选民的个体背景信息，要求它们预测每个人的投票行为。通过与实际结果对比，我们发现基于LLM的未来投票行为预测基本失败，其准确性在不同国家和语言语境中分布不均，且需要提示词中包含详细的态度信息。研究结果强调LLM合成样本在民意预测中的适用性有限。通过调查其语境偏差，本研究有助于理解和缓解LLM发展及其在计算社会科学应用中的不平等问题。
英文摘要：
"Synthetic samples" based on large language models (LLMs) have been argued to serve as efficient alternatives to surveys of humans, assuming that their training data includes information on human attitudes and behavior. However, LLM-synthetic samples might exhibit bias, for example due to training data and fine-tuning processes being unrepresentative of diverse contexts. Such biases risk reinforcing existing biases in research, policymaking, and society. Therefore, researchers need to investigate if and under which conditions LLM-generated synthetic samples can be used for public opinion prediction. In this study, we examine to what extent LLM-based predictions of individual public opinion exhibit context-dependent biases by predicting the results of the 2024 European Parliament elections. Prompting three LLMs with individual-level background information of 26,000 eligible European voters, we ask the LLMs to predict each person's voting behavior. By comparing them to the actual results, we show that LLM-based predictions of future voting behavior largely fail, their accuracy is unequally distributed across national and linguistic contexts, and they require detailed attitudinal information in the prompt. The findings emphasize the limited applicability of LLM-synthetic samples to public opinion prediction. In investigating their contextual biases, this study contributes to the understanding and mitigation of inequalities in the development of LLMs and their applications in computational social science.
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