Enhancing Product Description with LLM-Driven Structured Insights

About

In the competitive landscape of online retail, accurate and comprehensive product descriptions are crucial for both sellers and customers. While e-commerce platforms strive to provide detailed information, product descriptions often lack completeness or accuracy. Customer reviews can bridge this gap, but navigating a plethora of unstructured feedback can be challenging.

To address this issue, we introduce PRAISE: Product Review Attribute Insight Structuring Engine. This innovative tool leverages the power of Large Language Models (LLMs) to automatically analyze and synthesize product descriptions and customer reviews within a single, user-friendly interface.

By integrating these diverse data sources, PRAISE empowers both sellers and customers with a more comprehensive understanding of products, leading to improved decision-making and increased customer satisfaction.

Explore PRAISE and its capabilities through our web application, available here. The code repository for praise is available here

How does it work?

We introduce PRAISE, a tool designed to improve the clarity and informativeness of product descriptions by leveraging insights derived from customer reviews. PRAISE employs a hybrid approach, combining the analytical capabilities of Large Language Models (LLMs) with a rule-based methodology to effectively synthesize information from both product descriptions and customer feedback. PRAISE leverages the idea of structured outputs through LLMs. The core functionality of PRAISE can be broken down into these key steps:

• Step 1: Extracting Descriptive Details from Reviews: We employ an LLM to identify and extract important descriptive details about the product from each individual customer review. We also filter out opinions or irrelevant information in this step.
• Step 2: Comparison of Reviews with Seller Description and Categorization: We use an LLM to compare the descriptive details with the seller description to find information that is Matching, Partially Matching, Contradictory or Missing.
• Step 3: Grouping of Similar Attributes: To further refine the organization of information, similar attributes are grouped together using an LLM. This step ensures that related features are presented cohesively.
• Step 4: Generating Structured Insights: Finally, PRAISE generates structured insights that summarize the extracted details and their relationship to the seller description. This neatly structured output provides a clear and concise overview of the product's attributes.