Simulating Human Behavior with Cerebras
LlamaSim is a multi-LLM framework that aims to simulate human behavior at scale. Given a specific environment (e.g., voters in Pennsylvania, students at CMU), we simulate how target groups would respond to important questions/events. This allows us to more accurately predict event and condition outcomes.
One use case of LlamaSim is for predicting elections. Traditional polls and static models often fail to capture dynamic voter behavior, especially in battleground states where every news cycle can shift the race. Mass simulating LLMs brings a cutting-edge solution to predict elections more accurately than conventional methods.
For example, to simulate election results in battleground states, we can create 100s of LLMs that behave and role-play like real voters.
Simulating this type of interaction has traditionally been impractical due to slow inference speeds, which make group chat-style conversations cumbersome – especially at scale. This is very noticeable when using Autogen/CrewAI with base model providers. Moreover, simulating large groups of voters in real-time demands significant computational resources, with complexity only increasing as we scale to hundreds or even thousands of identities representing specific populations, such as voters from Pennsylvania.
