用用户评论推断性格特征，生成更贴心的外卖回复。
PARAN: Persona-Augmented Review ANswering system on Food Delivery Review Dataset
- 从短评中提取显性和隐性用户特征，注入生成提示。
- 无需微调模型，提升回复相关性和多样性。
- 适合做智能客服、个性化内容生成的场景。
个性化评论回复在用户信息有限的领域（如外卖平台）面临挑战。尽管大语言模型具备强大的文本生成能力，但缺乏上下文数据时常导致回复泛化，降低互动效果。本文提出一种两阶段提示框架，从短评中推断显性（如用户明确偏好）和隐性（如人口统计或表达风格线索）人物特征，并将这些特征融入生成提示，以生成定制化回复。为促进多样性与忠实度，推理时调整解码温度。我们在一个来自韩国外卖平台的真实数据集上评估该方法，考察其对精确度、多样性和语义一致性的提升。结果表明，通过人物增强提示可有效提升自动化回复的相关性与个性化水平，且无需模型微调。
原文摘要 · Abstract (English)
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.
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