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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights
%A Qidwai, Adnan
%A Mukhopadhyay, Srija
%A Khatiwada, Prerana
%A Roth, Dan
%A Gupta, Vivek
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F qidwai-etal-2025-praise
%X 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.
%R 10.18653/v1/2025.acl-demo.62
%U https://aclanthology.org/2025.acl-demo.62/
%U https://doi.org/10.18653/v1/2025.acl-demo.62
%P 644-652

Markdown (Informal)

[PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights](https://aclanthology.org/2025.acl-demo.62/) (Qidwai et al., ACL 2025)

• PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights (Qidwai et al., ACL 2025)

ACL

• Adnan Qidwai, Srija Mukhopadhyay, Prerana Khatiwada, Dan Roth, and Vivek Gupta. 2025. PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 644–652, Vienna, Austria. Association for Computational Linguistics.