The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents
This addresses the need for benchmarks to evaluate LLM-based agents against human collective behavior, though it is incremental in applying an existing human phenomenon to LLMs.
The paper examined whether LLM-based agents simulating partisan personas exhibit the 'wisdom of partisan crowds' phenomenon, where groups converge to more accurate beliefs through deliberation despite biases, and found they display human-like partisan biases and convergence, with factors like chain-of-thought prompts interfering and fine-tuning enhancing it.
Human groups are able to converge on more accurate beliefs through deliberation, even in the presence of polarization and partisan bias -- a phenomenon known as the "wisdom of partisan crowds." Generated agents powered by Large Language Models (LLMs) are increasingly used to simulate human collective behavior, yet few benchmarks exist for evaluating their dynamics against the behavior of human groups. In this paper, we examine the extent to which the wisdom of partisan crowds emerges in groups of LLM-based agents that are prompted to role-play as partisan personas (e.g., Democrat or Republican). We find that they not only display human-like partisan biases, but also converge to more accurate beliefs through deliberation as humans do. We then identify several factors that interfere with convergence, including the use of chain-of-thought prompt and lack of details in personas. Conversely, fine-tuning on human data appears to enhance convergence. These findings show the potential and limitations of LLM-based agents as a model of human collective intelligence.