Accurate and complete product descriptions are crucial for e-commerce, yet seller-provided information often falls short. Customer reviews offer valuable details but are laborious to sift through manually. We present PRAISE: Product Review Attribute Insight Structuring Engine, a novel system that uses Large Language Models (LLMs) to automatically extract, compare, and structure insights from customer reviews and seller descriptions. PRAISE provides users with an intuitive interface to identify missing, contradictory, or partially matching details between these two sources, presenting the discrepancies in a clear, structured format alongside supporting evidence from reviews. This allows sellers to easily enhance their product listings for clarity and persuasiveness, and buyers to better assess product reliability. Our demonstration showcases PRAISE's workflow, its effectiveness in generating actionable structured insights from unstructured reviews, and its potential to significantly improve the quality and trustworthiness of e-commerce product catalogs.

翻译：准确且完整的产品描述对于电子商务至关重要，但卖家提供的信息往往存在不足。客户评论提供了有价值的细节，但人工筛选费时费力。我们提出PRAISE：产品评论属性洞察结构化引擎，这是一个利用大语言模型自动从客户评论和卖家描述中提取、比较并结构化洞察的新型系统。PRAISE为用户提供了一个直观的界面，用于识别这两类信息源之间缺失、矛盾或部分匹配的细节，并以清晰的结构化格式呈现差异，同时附上来自评论的佐证。这使得卖家能够轻松完善其产品列表，提升描述的清晰度和说服力，同时帮助买家更好地评估产品可靠性。我们的演示展示了PRAISE的工作流程、其从非结构化评论中生成可操作结构化洞察的有效性，以及其在显著提升电子商务产品目录质量与可信度方面的潜力。