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.

翻译：本综述探讨了大型语言模型（LLMs）在电子商务领域的公平性问题，审视了其发展进程、实际应用及面临的挑战。LLMs已成为电子商务领域的关键技术，提供了创新解决方案并提升了客户体验。本文全面综述了LLMs在电子商务中的应用与挑战。文章首先介绍了LLMs在电子商务中应用的核心原理，详细阐述了为满足特定需求而进行的预训练、微调和提示工程等定制化流程。随后深入探讨了LLMs在电子商务中的多样化应用场景：包括产品评论分析——通过综合解析用户反馈；产品推荐系统——利用消费者数据推荐相关商品；产品信息翻译——提升全球可访问性；以及产品问答环节——实现客户支持自动化。本文重点批判性地分析了电子商务中的公平性挑战，指出训练数据和算法中的偏见可能导致不公平结果，例如强化刻板印象或对特定群体的歧视。这些问题不仅损害消费者信任，更引发伦理与法律层面的关切。最后，本文展望了未来研究方向，强调在电子商务领域开发更公平、更透明的LLMs的必要性，倡导通过持续努力减少偏见、提升系统公平性，确保这些技术能够以符合伦理的方式有效服务于全球多元化市场。通过系统性分析，本综述为当前LLMs在电子商务领域的发展格局提供了全景式视角，揭示了其潜力与局限，并为构建更公平、更包容的电子商务环境指明了未来研究方向。