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.

利用价值注入的大型语言模型 (VIM) 预测意见和行为的可行性与效果进行了系列实验，结果显示使用价值注入的大型语言模型可更好地预测人们的意见和行为，而且通过注入人类核心价值的变种，其性能显著优于基准方法。