Chain-of-Thought Prompting (CoT) reinforces the reasoning capabilities of Large Language Models (LLMs) through the generation of intermediate rationales. However, these enhancements predominantly benefit large-scale models, leaving small LMs without significant performance improvements when directly applying CoT. Despite the advanced reasoning capabilities of LLMs, CoT relies primarily on their pre-trained internal knowledge. The external knowledge that is previously unknown to the model remains unexploited. This omission becomes pronounced in tasks such as stance detection, where the external background knowledge plays a pivotal role. Additionally, the large-scale architecture of LLMs inevitably present efficiency challenges during deployment. To address these challenges, we introduce the Ladder-of-Thought (LoT) for stance detection. Grounded in a dual-phase Cascaded Optimization framework, LoT directs the model to incorporate high-quality external knowledge, enhancing the intermediate rationales it generates. These bolstered rationales subsequently serve as the foundation for more precise predictions - akin to how a ladder facilitates reaching elevated goals. LoT achieves a balance between efficiency and accuracy, making it an adaptable and efficient framework for stance detection. Our empirical evaluations underscore LoT's effectiveness, marking a 16% improvement over ChatGPT and a 10% enhancement compared to ChatGPT with CoT.

翻译：链式思维提示（CoT）通过生成中间推理过程增强了大型语言模型（LLMs）的推理能力。然而，这些提升主要使大规模模型受益，小型语言模型在直接应用CoT时无法获得显著的性能改进。尽管LLMs具有先进的推理能力，但CoT主要依赖其预训练的内部知识，而模型先前未知的外部知识仍未被利用。在立场检测等任务中，这种疏漏尤为明显——外部背景知识在其中起着关键作用。此外，LLMs的大规模架构在部署时不可避免会带来效率挑战。为解决这些问题，我们提出了面向立场检测的"思维阶梯"（LoT）。LoT基于双阶段级联优化框架，引导模型融入高质量外部知识，增强其生成的中间推理过程。这些增强的推理随后作为更精确预测的基础——正如阶梯助力攀登更高目标。LoT实现了效率与精度的平衡，成为适应性强且高效的立场检测框架。实证评估凸显了LoT的有效性，相较ChatGPT提升16%，较采用CoT的ChatGPT提升10%。