用语用理论作提示，让大模型更好理解言外之意
Pragmatic Theories Enhance Understanding of Implied Meanings in LLMs
- 用格赖斯语用学和关联理论做提示，引导模型逐步推理
- 相比基线提升最高9.6%，小模型效果更明显
- 仅提理论名称就能小幅增益，适合需要精准理解的场景
准确理解言外之意在人类交流中至关重要，语言模型也应具备此能力。本研究证明，将语用理论（如格赖斯语用学、关联理论）作为提示，是提升模型理解隐含意义的有效上下文学习方法。具体而言，我们提出在提示中引入语用理论概述，引导模型通过分步推理得出最终解释。实验结果表明，相比不提供语用理论的零样本思维链基线，该方法使模型在语用推理任务上得分最高提升9.6%。此外，即使不详细解释理论内容，仅在提示中提及理论名称，大型模型性能也能提升约1-3%。
原文摘要 · Abstract (English)
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.
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