用大模型自动分析评论，帮商家完善商品描述。
PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights
- 利用大模型从评论和描述中提取结构化信息
- 识别出描述缺失、矛盾或部分匹配的内容
- 适合电商卖家优化商品页，提升可信度
准确完整的商品描述对电子商务至关重要，但卖家提供的信息往往不充分。客户评论包含宝贵细节，却难以手动筛选。我们提出 PRAISE：产品评论属性洞察结构化引擎，利用大语言模型（LLMs）自动提取、比较并结构化来自客户评论和卖家描述的洞察。PRAISE 为用户提供直观界面，识别两源信息间的缺失、矛盾或部分匹配项，并以清晰结构呈现差异及评论证据。这使卖家能轻松优化商品列表以增强清晰度与说服力，买家也能更准确评估商品可靠性。演示展示了 PRAISE 的工作流程、从非结构化评论生成可操作洞察的有效性，以及显著提升电商商品目录质量与可信度的潜力。
原文摘要 · Abstract (English)
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.
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