Abstract
Personalized review response generation presents a significant challenge in domains where user information is limited, such as food delivery platforms. While large language models (LLMs) offer powerful text generation capabilities, they often produce generic responses when lacking contextual user data, reducing engagement and effectiveness. In this work, we propose a two-stage prompting framework that infers both explicit (e.g., user-stated preferences) and implicit (e.g., demographic or stylistic cues) personas directly from short review texts. These inferred persona attributes are then incorporated into the response generation prompt to produce user-tailored replies. To encourage diverse yet faithful generations, we adjust decoding temperature during inference. We evaluate our method using a real-world dataset collected from a Korean food delivery app, and assess its impact on precision, diversity, and semantic consistency. Our findings highlight the effectiveness of persona-augmented prompting in enhancing the relevance and personalization of automated responses without requiring model fine-tuning.
Abstract (translated)
个性化评论回复生成在用户信息有限的领域（如食品配送平台）中面临重大挑战。虽然大型语言模型（LLMs）提供了强大的文本生成能力，但当缺少上下文用户数据时，它们往往会产生通用且缺乏个性化的响应，从而降低参与度和有效性。为此，我们提出了一种两阶段提示框架，该框架可以直接从简短的评论文本中推断出显式的（如用户声明的偏好）和个人隐含的人格特征（如人口统计或风格线索）。然后将这些推断出来的人格属性整合到响应生成提示中，以产生个性化的回复。为了鼓励多样化且忠实的生成，在推理过程中调整解码温度。 我们使用从韩国食品配送应用程序收集的真实世界数据集评估了我们的方法，并对其在精度、多样性和语义一致性方面的效果进行了评价。研究结果表明，人格增强提示对于提高自动化响应的相关性和个性化程度非常有效，而无需对模型进行微调。
URL
https://arxiv.org/abs/2512.10148
