语言模型公平性定义详解：arXiv 论文 2407.18454 被撤稿
Fairness Definitions in Language Models Explained
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arXiv 管理员撤下了系统梳理语言模型公平性定义的综述论文 arXiv:2407.18454，原因是未达到 arXiv 的研究内容质量标准。该论文原提出按 encoder-only、decoder-only 与 encoder-decoder 架构分类的公平性概念新分类法，并通过实验说明各定义的实际影响。仓库曾公开在线提供，现该版本无 PDF 可用。
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Abstract:Language Models (LMs) have demonstrated exceptional performance across various Natural Language Processing (NLP) tasks. Despite these advancements, LMs can inherit and amplify societal biases related to sensitive attributes such as gender and race, limiting their adoption in real-world applications. Therefore, fairness has been extensively explored in LMs, leading to the proposal of various fairness notions. However, the lack of clear agreement on which fairness definition to apply in specific contexts and the complexity of understanding the distinctions between these definitions can create confusion and impede further progress. To this end, this paper proposes a systematic survey that clarifies the definitions of fairness as they apply to LMs. Specifically, we begin with a brief introduction to LMs and fairness in LMs, followed by a comprehensive, up-to-date overview of existing fairness notions in LMs and the introduction of a novel taxonomy that categorizes these concepts based on their transformer architecture: encoder-only, decoder-only, and encoder-decoder LMs. We further illustrate each definition through experiments, showcasing their practical implications and outcomes. Finally, we discuss current research challenges and open questions, aiming to foster innovative ideas and advance the field. The repository is publicly available online at this https URL.
| Comments: | arXiv admin note: This submission has been withdrawn because it does not meet arXiv's research content quality standards |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2407.18454 [cs.CL] |
| (or arXiv:2407.18454v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2407.18454 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1002/widm.70063
DOI(s) linking to related resources |
Submission history
From: arXiv Admin [view email]
[v1]
Fri, 26 Jul 2024 01:21:25 UTC (1,138 KB)
[v2]
Tue, 5 Aug 2025 20:22:54 UTC (6,795 KB)
[v3]
Thu, 15 Jan 2026 02:02:02 UTC (6,729 KB)
[v4]
Mon, 26 Oct 2026 20:38:22 UTC (1 KB) (withdrawn)
来源：arXiv cs.AI · arxiv.org