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LLMs Overconfident in Human Behavior Simulations, Study Finds

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv explores the use of token probabilities from large language models (LLMs) as confidence estimators in human behavior simulations. The research, focusing on predicting user ratings on a Luxembourgish news platform, found that models like Ministral 3 (3B and 8B) were overconfident and less accurate than a simple statistical baseline when using only in-context learning. While scaling the model improved accuracy, it degraded calibration. The study demonstrated that task-specific supervised fine-tuning significantly improved both accuracy and calibration, reducing Expected Calibration Error and increasing accuracy. AI

IMPACT Highlights the need for better calibration in LLMs for accurate human behavior simulation, potentially impacting agent development.

RANK_REASON Academic paper on LLM evaluation methodology. [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 →

LLMs Overconfident in Human Behavior Simulations, Study Finds

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Academic paper on LLM evaluation methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nils Schwager, Simon M\"unker, Alistair Plum, Achim Rettinger ·

    Evaluating Token Probabilities as Confidence Estimators in Human Behavior Simulations: A Case Study in Luxembourgish Next Like Prediction

    arXiv:2611.07735v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly used to simulate human behavior, their operational validity is typically assessed through accuracy alone, implicitly treating human behavior as deterministic. We argue that on noisy…

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