Large language models (LLMs) have transformed natural language processing across diverse fields, yet their general-purpose design limits their effectiveness in specialized domains, such as simulating opinions on environmental policies. This paper presents an approach for fine-tuning LLMs using data from the UK Household Longitudinal Study, improving the accuracy of opinion generation by conditioning models on socio-demographic factors like age, income, education, and region. By emulating diverse synthetic profiles, fine-tuned models capture the subtle differences across demographic groups more effectively than pre-trained versions. Metrics such as Chi-Squared, Cosine Similarity, Jaccard Index, and KL-divergence, demonstrate a strong alignment between synthetic and real-world opinion data. This approach highlights the potential of fine-tuning LLMs to provide more informed, representative, and ethical insights into public sentiments on environmental issues. The findings underscore the importance of tailoring LLMs to specific societal contexts for more accurate and ethical policy simulations.

本研究针对通用大型语言模型在模拟特定领域（如环境政策舆论）时的局限性，提出了一种利用英国家庭纵向研究的数据对模型进行微调的方法。其核心发现表明，通过考虑社会人口因素，微调后的模型在捕捉不同群体间的细微差异上效果显著优于预训练模型，从而为更准确、代表性和伦理的公众情绪洞察提供了创新性解决方案。

设计领域特定的大型语言模型：微调在公众舆论模拟中的关键作用