The Mask of Civility: Benchmarking Chinese Mock Politeness Comprehension in Large Language Models
It addresses gaps in pragmatic comprehension for Chinese language processing, though it is incremental as it applies existing frameworks to new data.
This study evaluated six large language models on their ability to recognize politeness, impoliteness, and mock politeness in Chinese discourse, finding performance differences across models and prompting strategies.
From a pragmatic perspective, this study systematically evaluates the differences in performance among representative large language models (LLMs) in recognizing politeness, impoliteness, and mock politeness phenomena in Chinese. Addressing the existing gaps in pragmatic comprehension, the research adopts the frameworks of Rapport Management Theory and the Model of Mock Politeness to construct a three-category dataset combining authentic and simulated Chinese discourse. Six representative models, including GPT-5.1 and DeepSeek, were selected as test subjects and evaluated under four prompting conditions: zero-shot, few-shot, knowledge-enhanced, and hybrid strategies. This study serves as a meaningful attempt within the paradigm of ``Great Linguistics,'' offering a novel approach to applying pragmatic theory in the age of technological transformation. It also responds to the contemporary question of how technology and the humanities may coexist, representing an interdisciplinary endeavor that bridges linguistic technology and humanistic reflection.