Summary of A Survey on Fairness Of Large Language Models in E-commerce: Progress, Application, and Challenge, by Qingyang Ren et al.
A survey on fairness of large language models in e-commerce: progress, application, and challenge
by Qingyang Ren, Zilin Jiang, Jinghan Cao, Sijia Li, Chiqu Li, Yiyang Liu, Shuning Huo, Tiange He, Yuan Chen
First submitted to arxiv on: 15 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have revolutionized e-commerce by providing innovative solutions and enhancing customer experiences. This survey explores the fairness of LLMs in e-commerce, examining their progress, applications, and challenges. The paper introduces the key principles underlying LLM usage, detailing pretraining, fine-tuning, and prompting processes that tailor models to specific needs. Applications include product reviews, recommendations, information translation, and question-answer sections. However, biases in training data and algorithms can lead to unfair outcomes, undermining consumer trust and raising ethical concerns. The survey outlines future research directions, emphasizing the need for equitable and transparent LLMs that mitigate biases and improve fairness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are helping e-commerce by being super smart and helpful. This paper looks at how they’re doing this, what problems they might cause, and how to make them fairer. Right now, they’re great at things like reading reviews, giving recommendations, and translating product info. But sometimes they can be unfair because of the way they were trained or the biases in their answers. We need to work on making these models more honest and transparent so we can trust them. |
Keywords
» Artificial intelligence » Fine tuning » Pretraining » Prompting » Translation
