April 3, 2024, 4:46 a.m. | Zhibo Chu, Zichong Wang, Wenbin Zhang

cs.CL updates on arXiv.org arxiv.org

arXiv:2404.01349v1 Announce Type: new
Abstract: Large Language Models (LLMs) have demonstrated remarkable success across various domains. However, despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations. Consequently, they may lead to discriminatory outcomes against certain communities, particularly marginalized populations, prompting extensive study in fair LLMs. On the other hand, fairness in LLMs, in contrast to fairness in traditional machine learning, entails exclusive backgrounds, taxonomies, and fulfillment techniques. To this end, this survey presents a …

abstract algorithms applications arxiv communities cs.ai cs.cl domains fair fairness however language language models large language large language models llms performance prompting study success survey type world

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