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        <title>Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce</title>
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    <abstract>In the dynamic field of eCommerce, the quality and comprehensiveness of product descriptions are pivotal for enhancing search visibility and customer engagement. Effective product descriptions can address the ‘cold start’ problem, align with market trends, and ultimately lead to increased click-through rates. Traditional methods for crafting these descriptions often involve significant human effort and may lack both consistency and scalability. This paper introduces a novel methodology for automating product description generation using the LLAMA 2.0 7B language model. We train the model on a dataset of authentic product descriptions from Walmart, one of the largest eCommerce platforms. The model is then fine-tuned for domain-specific language features and eCommerce nuances to enhance its utility in sales and user engagement. We employ multiple evaluation metrics—including NDCG, customer click-through rates, and human assessments—to validate the effectiveness of our approach. Our findings reveal that the system is not only scalable but also significantly reduces the human workload involved in creating product descriptions. This study underscores the considerable potential of large language models like LLAMA 2.0 7B in automating and optimizing various facets of eCommerce platforms, offering significant business impact, including improved search functionality and increased sales.</abstract>
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%0 Conference Proceedings
%T Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce
%A Zhou, Jianghong
%A Liu, Bo
%A Acharya, Jhalak
%A Hong, Yao
%A Lee, Kuang-Chih
%A Wen, Musen
%Y Gehrmann, Sebastian
%Y Wang, Alex
%Y Sedoc, João
%Y Clark, Elizabeth
%Y Dhole, Kaustubh
%Y Chandu, Khyathi Raghavi
%Y Santus, Enrico
%Y Sedghamiz, Hooman
%S Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhou-etal-2023-leveraging
%X In the dynamic field of eCommerce, the quality and comprehensiveness of product descriptions are pivotal for enhancing search visibility and customer engagement. Effective product descriptions can address the ‘cold start’ problem, align with market trends, and ultimately lead to increased click-through rates. Traditional methods for crafting these descriptions often involve significant human effort and may lack both consistency and scalability. This paper introduces a novel methodology for automating product description generation using the LLAMA 2.0 7B language model. We train the model on a dataset of authentic product descriptions from Walmart, one of the largest eCommerce platforms. The model is then fine-tuned for domain-specific language features and eCommerce nuances to enhance its utility in sales and user engagement. We employ multiple evaluation metrics—including NDCG, customer click-through rates, and human assessments—to validate the effectiveness of our approach. Our findings reveal that the system is not only scalable but also significantly reduces the human workload involved in creating product descriptions. This study underscores the considerable potential of large language models like LLAMA 2.0 7B in automating and optimizing various facets of eCommerce platforms, offering significant business impact, including improved search functionality and increased sales.
%U https://aclanthology.org/2023.gem-1.8
%P 88-96

Markdown (Informal)

[Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce](https://aclanthology.org/2023.gem-1.8) (Zhou et al., GEM-WS 2023)

• Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce (Zhou et al., GEM-WS 2023)

ACL

• Jianghong Zhou, Bo Liu, Jhalak Acharya, Yao Hong, Kuang-Chih Lee, and Musen Wen. 2023. Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce. In Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), pages 88–96, Singapore. Association for Computational Linguistics.