  This survey explores the fairness of large language models (LLMs) in
e-commerce, examining their progress, applications, and the challenges they
face. LLMs have become pivotal in the e-commerce domain, offering innovative
solutions and enhancing customer experiences. This work presents a
comprehensive survey on the applications and challenges of LLMs in e-commerce.
The paper begins by introducing the key principles underlying the use of LLMs
in e-commerce, detailing the processes of pretraining, fine-tuning, and
prompting that tailor these models to specific needs. It then explores the
varied applications of LLMs in e-commerce, including product reviews, where
they synthesize and analyze customer feedback; product recommendations, where
they leverage consumer data to suggest relevant items; product information
translation, enhancing global accessibility; and product question and answer
sections, where they automate customer support. The paper critically addresses
the fairness challenges in e-commerce, highlighting how biases in training data
and algorithms can lead to unfair outcomes, such as reinforcing stereotypes or
discriminating against certain groups. These issues not only undermine consumer
trust, but also raise ethical and legal concerns. Finally, the work outlines
future research directions, emphasizing the need for more equitable and
transparent LLMs in e-commerce. It advocates for ongoing efforts to mitigate
biases and improve the fairness of these systems, ensuring they serve diverse
global markets effectively and ethically. Through this comprehensive analysis,
the survey provides a holistic view of the current landscape of LLMs in
e-commerce, offering insights into their potential and limitations, and guiding
future endeavors in creating fairer and more inclusive e-commerce environments.
