探究大型语言模型的文化意识：跨文化美学文体学案例研究
Probing Cultural Awareness in LLMs: A Case Study of Cross-Culture Aesthetic Stylistics
AI总结：
通过构建C4STYLI基准（包含香港和中国大陆的高度风格化翻译电影片名和广告标语），评估大型语言模型在跨文化美学文体识别和生成方面的能力，发现模型依赖表层语言信息而非风格结构，对香港特定风格结构敏感度有限。
摘要：
大型语言模型（LLMs）越来越多地部署在多样化的文化背景中，但它们掌握美学文体学（即策略性地使用语言以唤起文化共鸣）的能力仍未得到充分探索。我们整理了C4STYLI，一个包含来自香港和中国大陆的高度风格化翻译电影片名和广告标语的基准，通过行为识别和生产能力的视角评估LLMs。广泛评估表明，LLMs在风格识别上与人类不同，且这种识别能力在不同文本领域有所变化。此外，LLMs中的风格识别和生成性能并不一致。为了进一步检查LLMs在风格识别中是否真正捕捉到风格信息，我们使用逻辑回归探针进行了结构消融。我们发现，在香港背景下，LLMs中的风格识别主要依赖于表层语言信息而非风格结构。这表明对香港特定风格结构的敏感度有限。
英文摘要：
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.
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