分析基于拉玛3的方法,从本体论中对公理的翻译
Xubing Hao1, Licong Cui1, Cui Tao2
1McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston.
CEUR workshop proceedings
|June 20, 2025
概括
大型语言模型 (LLM) 显示了将本体学公理转化为自然语言的潜力,有助于专家评估. 虽然Llama 3证明了一些准确性,但在本体论开发中的复杂公理需要进一步改进.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 知识表示 知识表示
背景情况:
- 本体论的开发依赖于工程师和领域专家之间的协作.
- 将复杂的本体学公理翻译成自然语言可以提高非专家利益相关者的理解能力.
- 评估本体论对于确保其准确性和实用性至关重要.
研究的目的:
- 研究大语言模型 (LLM) 在将本体学公理翻译成自然语言中的有效性.
- 评估LLM生成的翻译的潜力,以促进本体学评估.
- 在这个公理翻译任务中确定LLM的优点和局限性.
主要方法:
- 使用Llama 3,一个大型语言模型,用于公理翻译.
- 从五个已出版的本体学中翻译了1,192个本体学公理,跨越了19个不同的类型.
- 对翻译的公理进行了手动评估,以确定其准确性和表示质量.
主要成果:
- 13.67%的翻译公理是完全准确的,而22.48%是不准确的.
- 很大一部分 (63.84%) 的翻译是部分准确的.
- 法律学士在生成等级自然语言等价物方面表现出能力,但在复杂的公理方面遇到了困难.
结论:
- 通过公理翻译,LLM显示出支持在本体学中的知识工程的前景.
- 有机会通过短时间的培训或融入知识工程工作流程来提高LLM的绩效.
- 需要进一步的研究来完善LLM的能力,以准确和全面的本体论公理翻译.
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