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

研究发现大型语言模型在人类行为模拟中过于自信

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

一项新近发表在arXiv上的研究，探讨了将大型语言模型（LLMs）的令牌概率用作人类行为模拟中置信度估计器的可能性。该研究聚焦于预测卢森堡语新闻平台上用户的点赞行为，发现在仅使用上下文学习的情况下，Ministral 3（3B和8B）等模型表现出过度自信，并且准确性不如简单的统计基线。模型规模的扩大提高了准确性，但降低了校准度。研究表明，特定任务的监督微调显著提高了准确性和校准度，降低了预期校准误差并提高了准确性。 AI

影响强调了在准确模拟人类行为方面，大型语言模型需要更好的校准，这可能影响代理开发。

排序理由关于大型语言模型评估方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Nils Schwager, Simon M\"unker, Alistair Plum, Achim Rettinger · 2026-11-06 05:00

评估令牌概率作为人类行为模拟中的置信度估计器：以卢森堡语下一个词预测为例

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…

本文如何被排名

报道来源 [1]

评估令牌概率作为人类行为模拟中的置信度估计器：以卢森堡语下一个词预测为例

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