用大模型模拟公众意见，提升调查数据真实性和多样性。
Synthesizing Public Opinions with LLMs: Role Creation, Impacts, and the Future to eDemorcacy
- 通过角色注入与知识增强生成动态提问，模拟多元观点。
- 在选举研究数据上，生成意见与真实调查结果匹配度显著提升。
- 适合政策研究、舆情分析和民主技术探索者参考。
本文研究利用大语言模型（LLMs）合成公众意见，以应对传统调查方法中响应率下降和非响应偏差等问题。提出一种基于知识注入的角色创建方法，结合RAG与HEXACO人格模型及人口统计信息，实现上下文学习下的动态提示生成。该方法使LLM能更准确地模拟多样化观点，优于现有提示工程方案。基于合作选举研究（CES）问题的实验表明，该角色创建方法显著提升了大模型生成意见与真实人类调查结果的一致性，增强了回答契合度。同时讨论了当前挑战、局限性及未来研究方向。
原文摘要 · Abstract (English)
This paper investigates the use of Large Language Models (LLMs) to synthesize public opinion data, addressing challenges in traditional survey methods like declining response rates and non-response bias. We introduce a novel technique: role creation based on knowledge injection, a form of in-context learning that leverages RAG and specified personality profiles from the HEXACO model and demographic information, and uses that for dynamically generated prompts. This method allows LLMs to simulate diverse opinions more accurately than existing prompt engineering approaches. We compare our results with pre-trained models with standard few-shot prompts. Experiments using questions from the Cooperative Election Study (CES) demonstrate that our role-creation approach significantly improves the alignment of LLM-generated opinions with real-world human survey responses, increasing answer adherence. In addition, we discuss challenges, limitations and future research directions.
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