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Stance detection aims to identify the attitude expressed in a documenttowards a given target. Techniques such as Chain-of-Thought (CoT) promptinghave advanced this task, enhancing a model's reasoning capabilities through thederivation of intermediate rationales. However, CoT relies primarily on amodel's pre-trained internal knowledge during reasoning, thereby neglecting thevaluable external information that is previously unknown to the model. Thisomission, especially within the unsupervised reasoning process, can affect themodel's overall performance. Moreover, while CoT enhances Large Language Models(LLMs), smaller LMs, though efficient operationally, face challenges indelivering nuanced reasoning. In response to these identified gaps, weintroduce the Ladder-of-Thought (LoT) for the stance detection task.Constructed through a dual-phase Progressive Optimization Framework, LoTdirects the small LMs to assimilate high-quality external knowledge, refiningthe intermediate rationales produced. These bolstered rationales subsequentlyserve as the foundation for more precise predictions - akin to how a ladderfacilitates reaching elevated goals. LoT achieves a balance between efficiencyand performance. Our empirical evaluations underscore LoT's efficacy, marking a16% improvement over GPT-3.5 and a 10% enhancement compared to GPT-3.5 with CoTon stance detection task.

Ladder-of-Thought: Using Knowledge as Steps to Elevate Stance Detection