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建立词汇测试作为评估大型语言模型的基准

Gonzalo Martínez¹, Javier Conde², Elena Merino-Gómez³

¹Departamento de Ingeniería Telemática, Universidad Carlos III de Madrid, Leganés, Spain.

|December 12, 2024

概括

词汇测试对于评估大型语言模型 (LLM) 至关重要,揭示了它们的词汇知识中的重大差距. 恢复这些测试提供了对LLM语言能力的更全面的理解.

科学领域:

自然语言处理自然语言处理.
计算语言学计算语言学
人工智能的人工智能

背景情况:

传统的词汇测试对于语言建模评估至关重要,但现在被忽视了.
当前的大型语言模型 (LLM) 基准通常忽视了基本的语言方面,而是专注于特定任务或领域知识.
需要使用基础语言评估重新评估LLM.

研究的目的:

倡导恢复词汇测试作为评估LLM绩效的关键方法.
通过使用标准化词汇测试,调查著名的LLM的词汇知识.
识别和分析LLM词汇表示和学习机制中的差距.

主要方法:

评估七个不同的LLM,包括像Llama2,Mistral和GPT这样的模型.
使用了两种不同格式的词汇测试.
在两个语言中进行评估,以确保跨语言的有效性.

主要成果:

在评估的LLMs的词汇知识中发现了令人惊和显著的差距.
在不同的LLM架构和语言中观察到性能变化.
提供了对LLM词汇表示及其基础学习过程的复杂性的见解.

结论:

词汇测试仍然是全面的LLM评估的宝贵和必要的工具.
这些发现突显了当前LLM评估实践的局限性,以及需要采用更全面的方法.
自动化词汇测试生成为持续的LLM语言技能评估提供了一个可扩展的方法.

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