# PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights

arXiv: https://arxiv.org/abs/2506.17314 · HTML: https://mlsanity.com/paper/2506.17314/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders](https://mlsanity.com/paper/2403.03952/) (2024)
- [Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases](https://mlsanity.com/paper/2402.03099/) (2024)
- [An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce](https://mlsanity.com/paper/2403.00923/) (2024)
- [Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce](https://mlsanity.com/paper/2310.18357/) (2023)
- [SEOpinion: Summarization and Exploration Opinion of E-Commerce Websites](https://mlsanity.com/paper/2312.14171/) (2023)
- [Towards Robustness Analysis of E-Commerce Ranking System](https://mlsanity.com/paper/2403.04257/) (2024)

## Abstract (arXiv)

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.