无普遍礼貌：基于PLUM语料库的跨语言、多模型研究，探讨礼貌对LLM的影响
No Universal Courtesy: A Cross-Linguistic, Multi-Model Study of Politeness Effects on LLMs Using the PLUM Corpus
AI总结：
本文研究不同礼貌程度的用户提示对LLM响应的影响，发现礼貌程度对模型性能有显著影响，但这种影响因语言和模型而异。
摘要：
本文探讨了不同礼貌程度的用户提示对LLM响应的影响，发现礼貌程度对模型性能有显著影响，但这种影响因语言和模型而异。研究基于Brown和Levinson的礼貌理论和Culpeper的不礼貌框架，跨三种语言（英语、印地语、西班牙语）、五种模型（Gemini-Pro、GPT-4o Mini、Claude 3.7 Sonnet、DeepSeek-Chat和Llama 3）以及三种用户交互历史（原始、礼貌、不礼貌）进行实验。样本包含22,500对各种类型的提示和响应，通过一个八因素评估框架（连贯性、清晰度、深度、响应性、上下文保留、毒性、简洁性和可读性）评估五个礼貌程度水平。研究发现，模型性能高度受语气、对话历史和语言影响。虽然礼貌提示可使平均响应质量提升约11%，而不礼貌语气会降低质量，但这种影响在不同语言和模型中并不一致。英语最佳使用礼貌或直接语气，印地语最佳使用谦逊和间接语气，西班牙语最佳使用果断语气。在模型中，Llama对语气最敏感（11.5%范围），而GPT对对抗性语气更鲁棒。这些结果表明，礼貌是影响LLM行为的可量化计算变量，但其影响因语言和模型而异，而非普遍。为支持可重复性和未来工作，我们还发布了PLUM（Utterances中礼貌水平的多语言）公开语料库，包含1,500个经过人类验证的跨三种语言和五种礼貌类别的提示，并提供了六个可检验假设的正式补充分析，这些假设基于礼貌理论，经数据集实证评估。
英文摘要：
This paper explores the response of Large Language Models (LLMs) to user prompts with different degrees of politeness and impoliteness. The Politeness Theory by Brown and Levinson and the Impoliteness Framework by Culpeper form the basis of experiments conducted across three languages (English, Hindi, Spanish), five models (Gemini-Pro, GPT-4o Mini, Claude 3.7 Sonnet, DeepSeek-Chat, and Llama 3), and three interaction histories between users (raw, polite, and impolite). Our sample consists of 22,500 pairs of prompts and responses of various types, evaluated across five levels of politeness using an eight-factor assessment framework: coherence, clarity, depth, responsiveness, context retention, toxicity, conciseness, and readability. The findings show that model performance is highly influenced by tone, dialogue history, and language. While polite prompts enhance the average response quality by up to ~11% and impolite tones worsen it, these effects are neither consistent nor universal across languages and models. English is best served by courteous or direct tones, Hindi by deferential and indirect tones, and Spanish by assertive tones. Among the models, Llama is the most tone-sensitive (11.5% range), whereas GPT is more robust to adversarial tone. These results indicate that politeness is a quantifiable computational variable that affects LLM behaviour, though its impact is language- and model-dependent rather than universal. To support reproducibility and future work, we additionally release PLUM (Politeness Levels in Utterances, Multilingual), a publicly available corpus of 1,500 human-validated prompts across three languages and five politeness categories, and provide a formal supplementary analysis of six falsifiable hypotheses derived from politeness theory, empirically assessed against the dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
