The ability to accurately interpret implied meanings plays a crucial role in human communication and language use, and language models are also expected to possess this capability. This study demonstrates that providing language models with pragmatic theories as prompts is an effective in-context learning approach for tasks to understand implied meanings. Specifically, we propose an approach in which an overview of pragmatic theories, such as Gricean pragmatics and Relevance Theory, is presented as a prompt to the language model, guiding it through a step-by-step reasoning process to derive a final interpretation. Experimental results showed that, compared to the baseline, which prompts intermediate reasoning without presenting pragmatic theories (0-shot Chain-of-Thought), our methods enabled language models to achieve up to 9.6\% higher scores on pragmatic reasoning tasks. Furthermore, we show that even without explaining the details of pragmatic theories, merely mentioning their names in the prompt leads to a certain performance improvement (around 1-3%) in larger models compared to the baseline.

翻译：准确解读隐含意义的能力在人类交流与语言使用中至关重要，语言模型亦被期望具备此项能力。本研究证明，向语言模型提供语用理论作为提示是一种有效的上下文学习方法，可用于理解隐含意义的任务。具体而言，我们提出一种方法：将格赖斯语用学、关联理论等语用理论的概述作为提示呈现给语言模型，引导其通过逐步推理过程得出最终解释。实验结果表明，相较于仅提示中间推理而不提供语用理论的基线方法（零样本思维链），我们的方法使语言模型在语用推理任务中的得分最高提升9.6%。此外，我们发现即使不解释语用理论的具体细节，仅在提示中提及理论名称，也能使较大规模模型相比基线获得一定性能提升（约1-3%）。