用本地人角色生成的建议，和直接用目标语言生成的建议差别很大。
Personas Differ from Native-Language Generation: Language Pathways Shape LLM Interpersonal Advice
- 对比本地语言生成与本地人角色提示两种方式
- 角色提示使语气更亲昵但建议更不具体，且倾向选择对抗性方案
- 方法选择会影响跨语言研究结论，适合做文化比较研究者参考
大型语言模型被广泛用于人际建议及跨语言文化行为研究。常见做法是让模型以母语者身份作答。我们检验这种“母语者角色”是否等同于先用目标语言生成、再翻译回英文的结果。在13种语言、8个模型、600个社交问题上进行对比，评估语言风格、行为支持度和强制选择题中的行动推荐。结果发现，母语者角色（NP）与本地语言生成（NL）不可互换：相比NL，NP提升亲密度和积极语气，降低具体性与社会契合度；开放建议中提供的可操作支持更少。在强制选择题中，NP改变模型推荐动作，更倾向对抗而非回避，影响程度随语言、主题和模型而异。说明跨语言研究的方法选择会显著改变建议表达方式与行动推荐结果。
原文摘要 · Abstract (English)
LLMs are increasingly used for interpersonal advice and as tools for studying social behavior across languages and cultures. A common shortcut for eliciting language- or culture-related variation is to ask a model to answer as a native speaker. We test whether this native-speaker persona reproduces the outputs obtained when models instead generate advice in the target language and translate the response back into English. Using 600 interpersonal advice questions across 13 languages and eight LLMs, we compare native-language generation followed by translation (NL) with native-speaker persona prompting (NP), measuring linguistic style, behavioral scaffolding, and forced-choice action recommendations. We find that NP and NL are not interchangeable. Compared to NL, NP often increases lexical social cues, including affiliation and positive tone, while reducing qualities such as concreteness and social attunement; NP also provides less actionable scaffolding in open-ended advice. In forced-choice scenarios, NP changes which action the model selects, favoring confrontation over redirection, with effect sizes varying across languages, topics, and models. Our results show that cross-lingual elicitation strategy is a consequential methodological choice that can change both how advice is framed and which actions models recommend.
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