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English(EN) FactoryBench: Evaluating Industrial Machine Understanding

FactoryBench 基准测试揭示大型语言模型在工业机器理解方面存在困难

研究人员开发了 FactoryBench,这是一个新的基准测试,旨在利用工业机器人遥测数据评估时间序列模型和大型语言模型(LLMs)的机器理解能力。该基准测试包含超过 70,000 个问答对,分为四个因果层次,与 Pearl 的因果阶梯相呼应,并包含各种答案格式。对六个领先的大型语言模型的初步评估显示,在结构化任务上,没有一个模型的准确率超过 50%,在决策制定任务上,准确率更是未超过 18%,这凸显了当前人工智能理解工业机械能力方面的显著差距。 AI

影响 凸显了大型语言模型在工业应用能力方面的关键差距,可能指导未来在鲁棒机器理解方面的研究。

排序理由 该集群描述了一个用于评估人工智能模型的新学术基准测试。[lever_c_demoted from research: ic=1 ai=1.0]

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FactoryBench 基准测试揭示大型语言模型在工业机器理解方面存在困难

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该集群描述了一个用于评估人工智能模型的新学术基准测试。[lever_c_demoted from research: ic=1 ai=1.0]
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  1. arXiv cs.AI TIER_1 English(EN) · Philipp Petersen ·

    FactoryBench:评估工业机器理解能力

    We introduce FactoryBench, a benchmark for evaluating time-series models and LLMs on machine understanding over industrial robotic telemetry. Q&A pairs are organized along four causal levels (state, intervention, counterfactual, decision) instantiating Pearl's ladder of causation…