Leveraging Large Language Models for Enhanced Product Descriptions in
  eCommerce
Jianghong Zhou Bo Liu Jhalak Nilesh Acharya Yao Hong Kuang-chih Lee Musen Wen
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.