用大模型生成摘要，帮用户快速看清商品评论的优缺点。
ProdRev: A DNN framework for empowering customers using generative pre-trained transformers
- 用GPT-3的curie模型微调，实现生成式评论摘要。
- 模型参数超130亿，能理解评论间深层关系。
- 适合想高效决策但被海量评论困扰的网购用户。
疫情后，电商购物需求激增，单个商品的评论数量动辄上千，导致消费者面临决策瘫痪。现有商业工具虽通过评分机制提示评论可能被操纵，但难以帮助用户真正理解内容。本文提出ProdRev框架，基于生成式预训练变换器（GPT-3）的curie引擎，对超过130亿参数的大模型进行微调，实现非提取式的生成式摘要。该方法不仅提炼评论核心观点，更引入“常识推理”能力，揭示评论间的内在关联，避免简单拼接。最终输出结构化优缺点分析，帮助用户快速做出自主判断。
原文摘要 · Abstract (English)
Following the pandemic, customers, preference for using e-commerce has accelerated. Since much information is available in multiple reviews (sometimes running in thousands) for a single product, it can create decision paralysis for the buyer. This scenario disempowers the consumer, who cannot be expected to go over so many reviews since its time consuming and can confuse them. Various commercial tools are available, that use a scoring mechanism to arrive at an adjusted score. It can alert the user to potential review manipulations. This paper proposes a framework that fine-tunes a generative pre-trained transformer to understand these reviews better. Furthermore, using "common-sense" to make better decisions. These models have more than 13 billion parameters. To fine-tune the model for our requirement, we use the curie engine from generative pre-trained transformer (GPT3). By using generative models, we are introducing abstractive summarization. Instead of using a simple extractive method of summarizing the reviews. This brings out the true relationship between the reviews and not simply copy-paste. This introduces an element of "common sense" for the user and helps them to quickly make the right decisions. The user is provided the pros and cons of the processed reviews. Thus the user/customer can take their own decisions.
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