思维链提示引发知识增强
Chain of Thought Prompting Elicits Knowledge Augmentation
AI总结：
本文提出CoT-KA方法，利用大型语言模型的思维链提示为深度学习增强知识，无需额外检索或推理模型，在多个推理基准上优于纯思维链和非增强方法。
摘要：
知识增强的深度学习范式是指将领域知识识别并整合到深度模型中的一种范式。传统方法通常采用任务特定的方式从各种来源收集外部知识。相比之下，大型语言模型经过广泛预训练，可以作为外部知识的综合来源。在本文中，我们提出了CoT-KA，一种基于思维链的方法，用于为深度学习增强知识。CoT-KA避免了传统增强方法中所需的额外知识检索或知识推理模型。我们的结果表明，在十一个公开可用的各种推理任务基准中的大多数上，CoT-KA优于纯基于思维链的方法和非增强方法。
英文摘要：
The knowledge-augmented deep learning paradigm refers to a paradigm in which domain knowledge is identified and integrated into deep models. Conventional methods typically employ task-specific approaches to gather external knowledge from various sources. In contrast, large language models are extensively pre-trained and can serve as a comprehensive source of external knowledge. In this paper, we propose CoT-KA, a Chain-of-Thought-based method that augments knowledge for deep learning. CoT-KA avoids the need for additional knowledge retrieval or knowledge reasoning models, as required in conventional augmentation methods. Our results demonstrate that CoT-KA outperforms both pure CoT-based methods and the non-augmented method across the majority of eleven publicly available benchmarks for various reasoning tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
