English(EN) HouseholdBench: Evaluating Large Language Models as Predictors of Household Economic Behavior

新的基准HouseholdBench测试LLM在经济行为方面的能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了HouseholdBench，这是一个新的评估数据集，旨在评估大型语言模型（LLM）在预测家庭经济行为方面的能力。该基准结合了来自六项美国家庭调查的数据，涵盖了与消费、收入、劳动力、预期和住房相关的32个预测任务。初步评估显示，大多数LLM的表现优于基本基线，表现最好的模型在数值结果上的误差减少了12%以上，尽管梯度提升树模型通常表现更好。研究还发现，对一个拥有40亿参数的开放权重模型进行微调和聚合预测，可以显著提高其性能，使其与专有LLM相匹配。 AI

影响为评估LLM在经济预测方面的能力建立了新的标准，可能指导未来在社会经济应用方面的模型开发。

排序理由学术论文，介绍了一个用于评估LLM的新基准数据集。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Jin Huang, Diego Ferreras Garrucho, Yutong Xie, Walter M. Yuan, Qiaozhu Mei, Chen Lian, Jonathon Hazell · 2026-10-07 04:00

HouseholdBench：将大型语言模型作为家庭经济行为预测器的评估

arXiv:2610.07563v1 Announce Type: new Abstract: Large language models (LLMs) have the potential to meet a key goal in economics: a quantitative model of household decision making, across a variety of settings. Yet existing evaluations cover few surveys and outcomes, and do not st…

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HouseholdBench：将大型语言模型作为家庭经济行为预测器的评估

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