Large language models (LLMs) are increasingly capable of generating personalized, persuasive text at scale, raising new questions about bias and fairness in automated communication. This paper presents the first systematic analysis of how LLMs behave when tasked with demographic-conditioned targeted messaging. We introduce a controlled evaluation framework using three leading models: GPT-4o, Llama-3.3, and Mistral-Large-2.1, across two generation settings: Standalone Generation, which isolates intrinsic demographic effects, and Context-Rich Generation, which incorporates thematic and regional context to emulate realistic targeting. We evaluate generated messages along three dimensions: lexical content, language style, and persuasive framing. We instantiate this framework on climate communication and find consistent age- and gender-based asymmetries across models: male- and youth-targeted messages tend to emphasize more assertive and progressive framing, while female- and senior-targeted messages more often reflect warmth, care, and traditional themes. Contextual prompts systematically amplify these disparities, with persuasion scores being higher for male-targeted messages, while age-related differences vary across models. Our findings demonstrate how demographic stereotypes can surface and intensify in LLM-generated targeted communication, underscoring the need for bias-aware generation pipelines and transparent auditing frameworks that explicitly account for demographic conditioning in socially sensitive applications.

翻译：大语言模型（LLM）日益具备规模化生成个性化、有说服力文本的能力，引发了自动化传播中偏见与公平性的新问题。本文首次系统分析了当LLM执行基于人口统计条件的定向消息生成任务时的行为模式。我们提出一个受控评估框架，使用三种主流模型：GPT-4o、Llama-3.3和Mistral-Large-2.1，在两种生成场景下进行实验：独立生成（隔离内在人口统计效应）和上下文丰富生成（融入主题与区域语境以模拟真实定向场景）。我们从三个维度评估生成消息：词汇内容、语言风格和说服性框架。我们将该框架应用于气候传播领域，发现不同模型间存在一致的基于年龄和性别的非对称性：面向男性和青年的消息倾向于强调更坚定、进步的框架，而面向女性和老年人的消息则更多反映温暖、关怀和传统主题。上下文提示系统地放大了这些差异，面向男性的消息说服力评分更高，而年龄相关差异因模型而异。我们的研究结果表明，人口统计刻板印象如何在LLM生成的定向传播中显现和加剧，强调了在涉及社会敏感性的应用中，需要构建具备偏差感知能力的生成流水线和透明的审计框架，明确考虑人口统计条件化因素。