PARAN：基于食品配送评论数据集的个性化评论回复系统
PARAN: Persona-Augmented Review ANswering system on Food Delivery Review Dataset
AI总结：
PARAN通过从短评论文本中推断用户个性，提升食品配送评论回复的个性化和相关性，无需模型微调。
摘要：
在用户信息有限的领域，如食品配送平台，个性化评论回复生成是一个重大挑战。尽管大语言模型（LLMs）具有强大的文本生成能力，但缺乏上下文用户数据时，它们往往生成通用回复，降低参与度和有效性。在本工作中，我们提出了一种两阶段提示框架，该框架从短评论文本中推断出显性（例如，用户陈述的偏好）和隐性（例如，人口统计或风格提示）的个性。这些推断出的个性属性随后被纳入回复生成提示中，以生成针对用户的定制回复。为了鼓励多样且忠实的生成，我们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收集的真实世界数据集评估我们的方法，并评估其对精度、多样性和语义一致性的影响。我们的发现强调了个性化提示在增强自动回复的相关性和个性化方面的作用，而无需模型微调。
英文摘要：
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.
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。
