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【第151期】Humanity’s Last Exam
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：Humanity’s Last ExamSummary
"Humanity's Last Exam" (HLE) introduces a new benchmark designed to assess the knowledge of large language models (LLMs) at the frontier of human expertise. This dataset contains 3,000 multiple-choice and short-answer questions across various subjects, emphasizing deep reasoning skills and resistance to simple internet retrieval. The questions undergo a rigorous review process by subject-matter experts to ensure difficulty and quality. Evaluations reveal that current LLMs exhibit low accuracy and poor calibration on HLE, indicating a significant gap in capabilities. The authors suggest HLE offers a reference point for AI progress and informs discussions on AI risks and governance. The creation of the data was a global effort by almost 1000 expert contributors.
《人类最后的考试》（HLE）推出了一个新基准，旨在评估大型语言模型（LLMs）在接近人类专家前沿领域的知识水平。该数据集包含3000个多项选择题和简答题，涵盖多个学科，重点考察深度推理能力并避免简单的互联网检索。所有问题都经过了学科专家的严格审查，确保难度和质量。评估结果显示，当前的LLM在HLE上的准确性较低，且校准效果差，表明其能力存在显著差距。作者认为，HLE为AI进展提供了一个参考点，并为AI风险与治理的讨论提供了依据。该数据的创建是由近1000名专家贡献的全球合作成果。
原文链接：https://arxiv.org/abs/2501.14249
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By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：Humanity’s Last ExamSummary
"Humanity's Last Exam" (HLE) introduces a new benchmark designed to assess the knowledge of large language models (LLMs) at the frontier of human expertise. This dataset contains 3,000 multiple-choice and short-answer questions across various subjects, emphasizing deep reasoning skills and resistance to simple internet retrieval. The questions undergo a rigorous review process by subject-matter experts to ensure difficulty and quality. Evaluations reveal that current LLMs exhibit low accuracy and poor calibration on HLE, indicating a significant gap in capabilities. The authors suggest HLE offers a reference point for AI progress and informs discussions on AI risks and governance. The creation of the data was a global effort by almost 1000 expert contributors.
《人类最后的考试》（HLE）推出了一个新基准，旨在评估大型语言模型（LLMs）在接近人类专家前沿领域的知识水平。该数据集包含3000个多项选择题和简答题，涵盖多个学科，重点考察深度推理能力并避免简单的互联网检索。所有问题都经过了学科专家的严格审查，确保难度和质量。评估结果显示，当前的LLM在HLE上的准确性较低，且校准效果差，表明其能力存在显著差距。作者认为，HLE为AI进展提供了一个参考点，并为AI风险与治理的讨论提供了依据。该数据的创建是由近1000名专家贡献的全球合作成果。
原文链接：https://arxiv.org/abs/2501.14249