A survey on fairness of large language models in e-commerce: progress, application, and challenge

• URL: http://arxiv.org/abs/2405.13025v2
• Date: Fri, 21 Jun 2024 21:26:03 GMT
• Title: A survey on fairness of large language models in e-commerce: progress, application, and challenge
• Authors: Qingyang Ren, Zilin Jiang, Jinghan Cao, Sijia Li, Chiqu Li, Yiyang Liu, Shuning Huo, Tiange He, Yuan Chen,
• Abstract summary: This survey explores the fairness of large language models (LLMs) in e-commerce. It examines their progress, applications, and the challenges they face. The paper critically addresses the fairness challenges in e-commerce, highlighting how biases in training data and algorithms can lead to unfair outcomes.
• Score: 8.746342211863332
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• EcomEdit: An Automated E-commerce Knowledge Editing Framework for Enhanced Product and Purchase Intention Understanding [42.41707796705922]
  Knowledge Editing (KE) aims to correct and update factual information in Large Language Models (LLMs) to ensure accuracy and relevance without computationally expensive fine-tuning. ECOMEDIT is an automated e-commerce knowledge editing framework tailored for e-commerce-related knowledge and tasks.
  arXiv  Detail & Related papers  (2024-10-18T08:31:22Z)
• Information Discovery in e-Commerce [97.71958017283593]
  Information retrieval has a natural role to play in e-commerce, especially in connecting people to goods and services. The rise in popularity of e-commerce sites has made research on information discovery in e-commerce an increasingly active research area. Methods for information discovery in e-commerce largely focus on improving the effectiveness of e-commerce search and recommender systems.
  arXiv  Detail & Related papers  (2024-10-08T07:41:01Z)
• IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce [71.37481473399559]
  In this paper, we present IntentionQA, a benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce. IntentionQA consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline. Human evaluations demonstrate the high quality and low false-negative rate of our benchmark.
  arXiv  Detail & Related papers  (2024-06-14T16:51:21Z)
• A Reality check of the benefits of LLM in business [1.9181612035055007]
  Large language models (LLMs) have achieved remarkable performance in language understanding and generation tasks. This paper thoroughly examines the usefulness and readiness of LLMs for business processes.
  arXiv  Detail & Related papers  (2024-06-09T02:36:00Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• Online Advertisements with LLMs: Opportunities and Challenges [51.96140910798771]
  This paper explores the potential for leveraging Large Language Models (LLM) in the realm of online advertising systems. We introduce a general framework for LLM advertisement, consisting of modification, bidding, prediction, and auction modules.
  arXiv  Detail & Related papers  (2023-11-11T02:13:32Z)
• Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce [6.318353155416729]
  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. Our findings reveal that the system is not only scalable but also significantly reduces the human workload involved in creating product descriptions.
  arXiv  Detail & Related papers  (2023-10-24T00:55:14Z)
• Challenges and Contributing Factors in the Utilization of Large Language Models (LLMs) [10.039589841455136]
  This review explores the issue of domain specificity, where large language models (LLMs) may struggle to provide precise answers to specialized questions within niche fields. It's suggested to diversify training data, fine-tune models, enhance transparency and interpretability, and incorporate ethics and fairness training.
  arXiv  Detail & Related papers  (2023-10-20T08:13:36Z)
• LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following [16.800545001782037]
  This paper proposes LLaMA-E, the unified e-commerce authoring models that address the contextual preferences of customers, sellers, and platforms. We design the instruction set derived from tasks of ads generation, query-enhanced product title rewriting, product classification, purchase intent speculation, and general e-commerce Q&A. The proposed LLaMA-E models achieve state-of-the-art evaluation performance and exhibit the advantage in zero-shot practical applications.
  arXiv  Detail & Related papers  (2023-08-09T12:26:37Z)
• Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies [104.32199881187607]
  Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks. A promising approach to rectify these flaws is self-correction, where the LLM itself is prompted or guided to fix problems in its own output. This paper presents a comprehensive review of this emerging class of techniques.
  arXiv  Detail & Related papers  (2023-08-06T18:38:52Z)
• How Can Recommender Systems Benefit from Large Language Models: A Survey [82.06729592294322]
  Large language models (LLM) have shown impressive general intelligence and human-like capabilities. We conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.
  arXiv  Detail & Related papers  (2023-06-09T11:31:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.