arXiv:2603.16142v2 Announce Type: replace Abstract: Large language models (LLMs) have recently been adopted as synthetic agents for public opinion simulation, offering a promising alternative to costly and slow human surveys. Despite their scalability, current LLM-based simulation methods fail to capture social diversity, producing flattened inter-group differences and overly homogeneous responses across demographic groups. We identify this limitation as a Diversity Collapse phenomenon in LLM hidden representations, where distinct social identities become increasingly indistinguishable across
Researchers are actively identifying and addressing limitations in current large language model (LLM) applications for social simulation, with this paper specifically tackling the 'Diversity Collapse' phenomenon.
Accurate public opinion simulation with LLMs is crucial for policy-making, market research, and understanding social dynamics, making the fidelity of their representations of social diversity a critical issue.
The ability to inject and diversify parametric social identities in LLM simulations will lead to more nuanced and representative models of public opinion, improving their utility in real-world applications.
- · AI researchers
- · Social scientists
- · Market research firms
- · Policy makers
- · Traditional survey methods (in some contexts)
- · LLM developers not addressing diversity collapse
Public opinion simulations become more accurate and reflective of societal diversity.
Improved LLM simulation fidelity could lead to more effective policy interventions and product development strategies.
The enhanced capability of LLMs to model human populations may accelerate their adoption across various societal planning and prediction domains, raising ethical considerations about influence and representation.
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Read at arXiv cs.CL