CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations
Myra Cheng Tiziano Piccardi Diyi Yang
Recent work has aimed to capture nuances of human behavior by using LLMs to
simulate responses from particular demographics in settings like social science
experiments and public opinion surveys. However, there are currently no
established ways to discuss or evaluate the quality of such LLM simulations.
Moreover, there is growing concern that these LLM simulations are flattened
caricatures of the personas that they aim to simulate, failing to capture the
multidimensionality of people and perpetuating stereotypes. To bridge these
gaps, we present CoMPosT, a framework to characterize LLM simulations using
four dimensions: Context, Model, Persona, and Topic. We use this framework to
measure open-ended LLM simulations' susceptibility to caricature, defined via
two criteria: individuation and exaggeration. We evaluate the level of
caricature in scenarios from existing work on LLM simulations. We find that for
GPT-4, simulations of certain demographics (political and marginalized groups)
and topics (general, uncontroversial) are highly susceptible to caricature.