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Dansk(DA) General Phrase Debiaser: Debiasing Masked Language Models at a Multi-Token Level

新流程可消除语言模型中短语级别的刻板印象

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种“通用短语去偏器”（General Phrase Debiaser），这是一种旨在减轻掩码语言模型中短语级别偏差的新型流程。该方法从维基百科等来源识别刻板印象短语，然后在多词元级别上消除模型的偏差。实验表明，在不同学科和模型规模下，性别偏差显著减少，弥补了先前词级别去偏技术的不足。 AI

影响 通过开发减轻语言模型中短语级别偏差的方法，解决了人工智能安全领域的一个关键空白。

排序理由 该集群包含一篇研究论文，详细介绍了一种消除语言模型偏差的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新流程可消除语言模型中短语级别的刻板印象

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该集群包含一篇研究论文，详细介绍了一种消除语言模型偏差的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 Dansk(DA) · Bingkang Shi, Xiaodan Zhang, Dehan Kong, Yulei Wu, Zongzhen Liu, Honglei Lyu, Longtao Huang ·

    General Phrase Debiaser: 在多令牌级别上消除掩码语言模型的偏差

    arXiv:2311.13892v4 Announce Type: replace-cross Abstract: The social biases and unwelcome stereotypes revealed by pretrained language models are becoming obstacles to their application. Compared to numerous debiasing methods targeting word level, there has been relatively less at…

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