May 26, 2026 · cs.CLJ/K move · Enter open · S save
Jiashuo Wang, Fenggang Yu, Jian Wang, Chak Tou Leong+5
Department of Computing, Hong Kong Polytechnic University · Institute of Digital Twin, Eastern Institute of Technology, Ningbo · Department of Language Science and Technology, Hong Kong Polytechnic University · School of Design, Hong Kong Polytechnic University · Research Institute for Quantum Technology, Hong Kong Polytechnic University
Large Language Models (LLMs) are increasingly deployed in diverse cultural contexts, yet their ability to master aesthetic stylistics, i.e., the strategic use of language to evoke cultural resonance, remains underexplored. We curate C4STYLI, a benchmark of highly stylized translated movie titles and advertising slogans from Hong Kong and the Chinese Mainland, to evaluate LLMs via the lens of behavioral recognition and productive competence. Extensive evaluations show that LLMs differ from humans in stylistic recognition, and this recognition ability varies across text domains. In addition, stylistic recognition and generation performance in LLMs are not consistently aligned. To further examine whether LLMs genuinely capture stylistic information in stylistic recognition, we conduct structural ablation with logistic regression probes. We find that, in the Hong Kong setting, stylistic recognition in LLMs relies primarily on surface-level linguistic information rather than stylistic structure. This suggests limited sensitivity to Hong Kong-specific stylistic structure.