  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.
