AF:一个行动框架,自行检查大语言模型的理解自相一致性
Huihui Shao1, Fanyu Wang2, Zhenping Xie1
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China; Jiangsu Key University Laboratory of Software and Media Technology under Human-Computer Cooperation (Jiangnan University), Wuxi, China.
概括
大型语言模型 (LLM) 显示对隐性文本关系的理解有所不同. 一个新的框架,S2AF,测量这种自我一致性,并可以提高LLM的表现.
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.
- 认知科学 认知科学
背景情况:
- 大型语言模型 (LLM) 显示出先进的语言理解,但它们对隐性文本关系的理解尚未完全理解.
- 评估LLM输出中的知识深度和逻辑一致性对于可靠的AI开发至关重要.
研究的目的:
- 引入"理解自我一致性"作为LLM保持逻辑和上下文连贯性的能力的指标.
- 提出并评估自问自答框架 (S2AF) 来衡量这种自我一致性.
主要方法:
- 开发了S2AF,一个具有自我质疑和回答机制的框架,为自主生成,质疑,回答和评估创建了一个闭环.
- 在两个数据集中的六个LLM上测试了S2AF,以客观地测量它们的自我一致性和知识关系理解.
主要成果:
- 法律学士表现出可衡量的"理解自我一致性",具有跨推理范式的差异化能力.
- 通过"自我增强的前"机制,可以通过自己的输出来提高LLM的绩效.
- S2AF利用事实上的逻辑关系,独立于外部数据.
结论:
- "理解自我一致性"为LLM知识表示提供了一个新的视角.
- S2AF框架提供了一种强大的方法来评估和改进LLM的逻辑连贯性.
- 研究结果表明,通过增强的LLM自我一致性,可以推进嵌入式人工智能 (EAI).
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