Being able to predict people's opinions on issues and behaviors in realistic scenarios can be helpful in various domains, such as politics and marketing. However, conducting large-scale surveys like the European Social Survey to solicit people's opinions on individual issues can incur prohibitive costs. Leveraging prior research showing influence of core human values on individual decisions and actions, we propose to use value-injected large language models (LLM) to predict opinions and behaviors. To this end, we present Value Injection Method (VIM), a collection of two methods -- argument generation and question answering -- designed to inject targeted value distributions into LLMs via fine-tuning. We then conduct a series of experiments on four tasks to test the effectiveness of VIM and the possibility of using value-injected LLMs to predict opinions and behaviors of people. We find that LLMs value-injected with variations of VIM substantially outperform the baselines. Also, the results suggest that opinions and behaviors can be better predicted using value-injected LLMs than the baseline approaches.

翻译：在现实场景中预测人们对具体议题的观点和行为，对政治学和市场营销等多个领域具有重要价值。然而，开展诸如欧洲社会调查这类大规模民意调研来收集个体意见，往往会产生高昂成本。基于核心人类价值观影响个体决策与行为的既有研究，我们提出使用价值注入式大型语言模型（LLM）来预测观点与行为。为此，我们设计了价值注入方法（VIM），该方法包含两种技术——论点生成与问答系统——旨在通过微调将目标价值分布注入语言模型。随后，我们在四项任务中开展系列实验，验证VIM的有效性及利用价值注入式语言模型预测人类观点与行为的可行性。实验结果显示，采用VIM变体注入价值观的语言模型显著优于基准模型。此外，研究结果表明，相较于基线方法，价值注入式语言模型能更有效地预测观点和行为。