分析LLM生成文本中说服性语言的差异：揭示刻板的性别模式
Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns
AI总结：
提出框架评估LLM生成说服性语言时受接收者性别、发送者意图和输出语言的影响，发现所有模型均存在显著的性别差异，反映性别刻板印象的语言倾向。
摘要：
大型语言模型（LLMs）越来越多地用于日常交流任务，包括起草旨在影响和说服的人际信息。先前研究表明，LLMs能够成功说服人类并放大说服性语言。因此，理解用户指令如何影响说服性语言的生成，以及生成的说服性语言是否因目标群体不同而有所差异至关重要。在这项工作中，我们提出了一个框架，用于评估说服性语言生成如何受接收者性别、发送者意图或输出语言的影响。我们使用成对提示指令评估了13个LLMs和16种语言。我们采用基于社会心理学和传播科学的LLM-as-judge设置，在19个说服性语言类别上评估模型响应。我们的结果揭示了所有模型生成的说服性语言中存在显著的性别差异。这些模式反映了与社会心理学和社会语言学中记录的性别刻板语言倾向一致的偏见。
英文摘要：
Large language models (LLMs) are increasingly used for everyday communication tasks, including drafting interpersonal messages intended to influence and persuade. Prior work has shown that LLMs can successfully persuade humans and amplify persuasive language. It is therefore essential to understand how user instructions affect the generation of persuasive language, and to understand whether the generated persuasive language differs, for example, when targeting different groups. In this work, we propose a framework for evaluating how persuasive language generation is affected by recipient gender, sender intent, or output language. We evaluate 13 LLMs and 16 languages using pairwise prompt instructions. We evaluate model responses on 19 categories of persuasive language using an LLM-as-judge setup grounded in social psychology and communication science. Our results reveal significant gender differences in the persuasive language generated across all models. These patterns reflect biases consistent with gender-stereotypical linguistic tendencies documented in social psychology and sociolinguistics.
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。
