ProdRev: A DNN framework for empowering customers using generative pre-trained transformers
This addresses a specific issue for e-commerce consumers by offering a tool to process reviews more effectively, though it appears incremental as it builds on existing GPT models for a domain-specific application.
The paper tackles the problem of decision paralysis for e-commerce customers overwhelmed by thousands of product reviews by proposing a framework that fine-tunes a generative pre-trained transformer (GPT-3 with over 13 billion parameters) for abstractive summarization, providing pros and cons to help users make better decisions.
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.