Prompt engineering and calibration make large language models excel at reasoning tasks, including multiple choice commonsense reasoning. From a practical perspective, we investigate and evaluate these strategies on smaller language models. Through experiments on five commonsense reasoning benchmarks, we find that each strategy favors certain models, but their joint effects are mostly negative.

翻译：提示工程与校准使大语言模型在推理任务（包括多项选择常识推理）中表现卓越。从实践角度出发，我们在较小语言模型上研究并评估了这些策略。通过对五个常识推理基准的实验发现，每种策略均对特定模型有利，但它们的联合效应大多为负。