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GATmath和GATLc:评估阿拉伯大语言模型的综合基准

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概括
此摘要是机器生成的。

开发阿拉伯大型语言模型 (LLM) 是一个挑战, 新的数据集GATmath和GATLc提供了大规模的推理和语言任务,以推动阿拉伯人工智能的发展.

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科学领域:

  • 人工智能
  • 自然语言处理
  • 计算语言学

背景情况:

  • 大型语言模型 (LLM) 已经推进了人工智能,但它们的发展需要强有力的评估.
  • 缺乏全面的基准和评估工具阻碍了对阿拉伯语LLM的评估.
  • 这种稀缺性限制了阿拉伯语言模型的进步和实际应用.

研究的目的:

  • 介绍GATmath (7千个问题) 和GATLc (9千个问题),这是阿拉伯语中多任务推理和语言理解的新标准.
  • 提供首个专门为阿拉伯语言设计的大规模,全面的推理数据集.
  • 促进严格的评估,并推动阿拉伯LLM的发展.

主要方法:

  • 创建了两个大规模的阿拉伯语数据集,GATmath和GATLc,来自一般能力测试 (GAT).
  • 数据集包括各种类别,需要推理,语义分析,语言理解和数学问题解决.
  • 在这些新开发的基准上评估了七个著名的LLM.

主要成果:

  • 最高效的LLM只达到66.9% (GATmath) 和64.3% (GATLc) 的准确性.
  • 这些结果凸显了GATmath和GATLc数据集所带来的重大困难.
  • 目前最先进的LLM在阿拉伯语推理和语言理解方面存在重大局限性.

结论:

  • 对于现有的阿拉伯LLM来说,GATmath和GATLc数据集是一个相当大的挑战.
  • 在开发更有能力的阿拉伯语言模型方面还有很大的改进空间.
  • 这些基准对于推进阿拉伯人工智能研究和开发至关重要.