心智推理方式:增强类型感知,以实现对文本的通用零射线逻辑推理.
IEEE transactions on neural networks and learning systems
|September 29, 2023
概括
本研究介绍了ZsLR,一个通用零射击逻辑推理的基准,以及TaCo,一种类型意识模型. 塔科增强模型对推理类型的感知,在各种环境中表现优于最先进的方法.
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.
- 机器学习 机器学习
背景情况:
- 逻辑推理任务,通常被定义为多选题答案 (MCQA),通常假设训练和测试数据之间的推理类型分布相似.
- 这种假设在现实应用中经常被违反,需要研究零射击能力和改进推理类型感知.
研究的目的:
- 通过提出一个新的框架来解决当前逻辑推理基准的局限性,为一般化零射击逻辑推理提出一个新的框架.
- 开发一个模型,可以在训练期间有效地感知和概括不同类型的逻辑推理,而无需明确的类型监督.
主要方法:
- 引入了ZsLR (零射击逻辑推理),这是一个基于三种类型抽样策略的六个分割的基准,用于评估一般化的零射击性能.
- 提出了TaCo (类型意识模型),它采用启发式输入重建和用全局节点构建文本图形.
- 在TaCo内部集成图形推理和对比学习,以增强对全球表示中的推理类型的感知.
主要成果:
- 在零射击和全数据设置中,TaCo在最先进的 (SOTA) 方法中表现出优越性.
- 该模型的概括能力在其他逻辑推理数据集上得到了验证.
- 实验证实了TaCo在改善逻辑推理任务的类型感知方面的有效性.
结论:
- 拟议的ZsLR基准和TaCo模型为一般化的零射击逻辑推理提供了显著的进步.
- 塔科有效地提高了对不同推理类型的模型理解和概括,解决了现有方法的关键局限性.
- 这些发现表明TaCo在复杂的基于文本的推理任务中具有更广泛的应用潜力.
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