We consider the problem of aligning a large language model (LLM) to model the
preferences of a human population. Modeling the beliefs, preferences, and
behaviors of a specific population can be useful for a variety of different
applications, such as conducting simulated focus groups for new products,
conducting virtual surveys, and testing behavioral interventions, especially
for interventions that are expensive, impractical, or unethical. Existing work
has had mixed success using LLMs to accurately model human behavior in
different contexts. We benchmark and evaluate two well-known fine-tuning
approaches and evaluate the resulting populations on their ability to match the
preferences of real human respondents on a survey of preferences for battery
electric vehicles (BEVs). We evaluate our models against their ability to match
population-wide statistics as well as their ability to match individual
responses, and we investigate the role of temperature in controlling the
trade-offs between these two. Additionally, we propose and evaluate a novel
loss term to improve model performance on responses that require a numeric
response.

对使用大型语言模型 (LLM) 来模拟人群的偏好进行了研究和评估，包括模拟焦点小组、虚拟调查和测试行为干预的应用，以及使用两种知名精调方法对电池电动汽车 (BEVs) 偏好调查的人群进行评估。同时，还提出并评估了一种新的损失项以改善对需要数字回答的问题的模型性能。