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LLM agents struggle to simulate diverse human values in social science research

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv explores the ability of large language model (LLM) agents to accurately simulate diverse human value systems in social science research. The research found that over 50% of simulated personas failed to represent their assigned value profiles from the beginning, with an additional 2-7% drifting over time. While LLM agents can produce plausible conversations, they currently struggle to faithfully represent and maintain distinct human value profiles, suggesting limitations for their use as proxies in social science research. AI

IMPACT Current LLM agents are limited in their ability to accurately simulate diverse human value systems, impacting their use in social science research.

RANK_REASON Research paper published on arXiv detailing limitations of LLM agents in simulating human values. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM agents struggle to simulate diverse human values in social science research

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Research paper published on arXiv detailing limitations of LLM agents in simulating human values. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Farah Atif, Sougata Saha, Monojit Choudhury ·

    The Failure Happens Before the Drift: The Social Dynamics of Values in LLM Agent Societies

    arXiv:2609.05514v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents are increasingly used as proxies for human participants in social science research, yet it remains unclear whether they can faithfully simulate diverse and conflicting human value systems. We …

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