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New framework reveals LLMs struggle to simulate inconsistent human behavior

By PulseAugur Editorial · [1 sources] ·

A new evaluation framework called CoCoEval has been developed to assess large language models (LLMs) in simulating human social interactions, specifically focusing on inconsistent and uncollaborative behaviors. Researchers found that current LLMs like GPT-4.1, GPT-5.1, and Claude Opus 4 exhibit significantly fewer such behaviors than humans, and that prompt engineering and fine-tuning do not reliably bridge this gap. The study raises concerns about the accuracy of LLMs as proxies for genuine human social dynamics. AI

IMPACT Highlights limitations in LLM's ability to accurately simulate nuanced human social interactions, impacting their use in social science research.

RANK_REASON Research paper introducing a new evaluation framework for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

New framework reveals LLMs struggle to simulate inconsistent human behavior

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Research paper introducing a new evaluation framework for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ryo Kamoi, Ameya Godbole, Binglin Zhou, Xiaoxin Lu, Longqi Yang, Rui Zhang, Mengting Wan, Pei Zhou ·

    Evaluating LLM-Simulated Conversations in Modeling Inconsistent and Uncollaborative Behaviors in Human Social Interaction

    arXiv:2603.17094v2 Announce Type: replace Abstract: Simulating human conversations using large language models (LLMs) has emerged as a scalable methodology for modeling human social interaction. This paper reconsiders the evaluation of simulated conversations by explicitly recogn…

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