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English(EN) Tracing Stereotypes from Representation to Output in Multilingual LLMs

研究探究多语言大语言模型中的刻板印象表征

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新研究论文调查了刻板印象如何在多语言大语言模型（LLMs）中显现。该研究比较了诸如Llama-3.1-8B、Qwen3-8B和Gemma-2-9B等模型上的线性探测和稀疏自编码器等多种方法，以了解刻板印象相关信息在何处表示以及它如何影响输出。研究结果表明，在这些模型中，探测器的性能峰值远早于归因，并且一小部分特征表现出语言无关的影响，但没有一个是完全类别无关的。 AI

影响 这项研究为理解多语言大语言模型中偏见的编码和传播方式提供了见解，可能指导未来开发更公平、更平等的人工智能系统。

排序理由 该集群包含一篇详细介绍大语言模型行为研究的学术论文。

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研究探究多语言大语言模型中的刻板印象表征

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ariun-Erdene Tumurchuluun, Yusser Al Ghussin, Pinzhen Chen, Josef van Genabith, Koel Dutta Chowdhury ·

    Tracing Stereotypes from Representation to Output in Multilingual LLMs

    arXiv:2609.08322v1 Announce Type: cross Abstract: Multilingual LLMs show stereotype-related behavior that varies across languages, but behavioral scores do not show where the relevant information is represented or how it affects the output. To investigate these internal mechanism…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    多语言大模型中从表征到输出的刻板印象追踪

    Multilingual LLMs show stereotype-related behavior that varies across languages, but behavioral scores do not show where the relevant information is represented or how it affects the output. To investigate these internal mechanisms, we compare linear probing, attribution patching…

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