在LLMs上进行微调或提示:评估知识图表构建任务.
Hussam Ghanem1,2, Christophe Cruz1
1Université Bourgogne Europe, CNRS, Laboratoire Interdisciplinaire Carnot de Bourgogne ICB UMR 6303, Dijon, France.
Frontiers in big data
|July 10, 2025
概括
本研究比较了使用大型语言模型 (LLM) 从文本中构建知识图的方法. 微调 (FT) 成为最强的方法,数据集大小显著影响性能.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 知识表示 知识表示
背景情况:
- 从非结构化文本构建知识图 (KG) 对数据组织和检索至关重要.
- 大型语言模型 (LLM) 为自动化KG创建提供了新的可能性.
- 评估不同的基于LLM的KG构建方法对于实际应用至关重要.
研究的目的:
- 为了比较零拍摄提示,少数拍摄提示和微调 (FT) 对于文本到知识图 (T2KG) 构建.
- 评估包括Llama2,Mistral和Starling在内的各种LLM在T2KG任务中的表现.
- 确定影响T2KG建设效率的关键因素,并提出未来研究方向.
主要方法:
- 使用三个提示策略进行实验:零射击,少数射击和微调 (FT).
- 利用Llama2,Mistral和Starling等最先进的LLM用于T2KG建设.
- 开发和应用细微的评估指标来评估KG质量.
主要成果:
- 微调 (FT) 与零拍摄和少数拍摄提示相比,表现优越.
- 培训数据集的大小显著影响构建的知识图的质量.
- 细微的评估指标揭示了不同方法的具体优缺点.
结论:
- 微调 (FT) 是使用LLMs构建文本到知识图 (T2KG) 的高效方法.
- 数据集规模是基于LLM的KG生成成功的关键决定因素.
- 未来的工作应该专注于改进评估指标,整合同义词意识,并探索LLM驱动的数据增强.
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