使用大型语言模型进行因果关系分析的挑战和机遇
1Computer Science & Data Science, University of North Carolina Charlotte, Charlotte, NC 28223, USA.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
大型语言模型 (LLM) 显示出从文本中提取因果信息的潜力,在分析中达成高度一致. 这些模型为理解传统注释方法之外的因果关系提供了新的可能性.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 从文本中提取因果关系对于理解复杂的关系至关重要.
- 现有的方法在准确识别因果关系方面面临挑战.
- 对因果关系的各种概念化 (例如,亚里士多德,珍珠) 影响了提取方法.
研究的目的:
- 检查大型语言模型 (LLM) 在基于文本的因果关系提取中的功能和局限性.
- 探索LLM如何处理各种因果关系观点并提高分析可靠性.
- 讨论LLM驱动的因果关系研究的未来方向.
主要方法:
- 审查现有的数据集和机器学习方法,以提取因果关系.
- 对高级LLM (例如GPT-5,Gemini 2.5 Pro) 进行因果关系分析任务的评估.
- 实验分析LLM在确定因果关系和协议率方面的表现.
主要成果:
- 法学士在进行因果关系分析方面表现出显著的能力,包括反事实和亚里士多德观点.
- 新的LLM在识别因果关系方面达到了很高的协议 (88-91%),超过了传统的注释者间协议.
- 法律学士可以解释和批评因果分析,表明他们朝着语义理解的方向迈进,而不仅仅是单词跨度.
结论:
- 法律法规为推进因果关系提取提供了巨大的机会,解决了数据稀疏性问题.
- 文本中因果关系分析的未来可能会从基于注释的转向由LLMs促进的语义理解.
- 在未来的代中,LLM显示出克服幻觉和缺乏常识等局限性的潜力.
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