发表机构
Université de Technologie de Compiègne; Norwegian University of Science and Technology (NTNU); SimulaMet; HUTECH University（贡比涅技术大学; 挪威科技大学; 西穆拉梅特研究院; 胡志明市技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究以5个大语言模型为对象，在4种语言条件下评估68个非翻译问题的响应，发现提示词语言会显著影响响应长度，且对多语言提示词设计具有启示意义。
AI 中文摘要
本研究考察提示词与响应语言如何影响大语言模型（LLM）的行为。我们使用5个模型，针对68个非翻译问题，在4种语言条件下评估其答案：英语到英语、英语到挪威语、挪威语到挪威语、挪威语到英语。剔除被弃权（不执行）的条目后，数据集包含1348条响应。我们采用Cohen d衡量长度差异，用LabSE余弦相似度衡量语义保真度，用原始Jaccard和软Jaccard衡量跨语言关键词重叠。提示词语言对响应长度有显著影响：当输出为英语时，挪威语提示词使响应缩短约37%；当输出为挪威语时，英语提示词使响应缩短约41%。最大的跨语言对比显示单词数减少但标记数减少幅度较小，反映了分词器的差异。尽管长度存在差异，语义相似度仍保持较高水平，且软Jaccard揭示了原始Jaccard未捕捉到的大量概念重叠。效应量随模型变化，存在异质性。提示词语言并非中性，会系统性地塑造输出长度与词汇实现，对多语言提示词设计具有启示意义。
英文摘要
This study examines how prompt and response language influence the behavior of large language models. Using five models, we evaluated answers to 68 non translation questions across four language conditions: English to English, English to Norwegian, Norwegian to Norwegian, and Norwegian to English. After removing refused items, the dataset contains 1348 responses. We measure length differences with Cohen d, semantic fidelity with LabSE cosine similarity, and cross lingual keyword overlap with both raw and soft Jaccard. Prompt language has a strong effect on response length. With English output, Norwegian prompts shorten responses by about thirty seven percent. With Norwegian output, English prompts shorten responses by about forty one percent. The largest cross lingual contrast shows a reduction in word count but a smaller reduction in tokens, reflecting tokenizer differences. Despite variation in length, semantic similarity remains high, and soft Jaccard reveals substantial conceptual overlap that raw Jaccard does not capture. Effect sizes vary across models, indicating heterogeneity. Prompt language is not neutral and systematically shapes output length and lexical realization, with implications for multilingual prompt design.
