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English(EN) Are Non-English Papers Reviewed Fairly? Language-of-Study Bias in NLP Peer Reviews

研究发现非英语自然语言处理论文面临不公平的同行评审偏见

作者 PulseAugur 编辑部 · [1 个来源] ·

一项新近发表在arXiv上的研究调查了自然语言处理（NLP）同行评审中的研究语言偏见。研究人员发现，与仅关注英语的论文相比，专注于非英语语言的论文面临着显著更高的负面偏见率。该研究引入了一个名为LOBSTER的数据集和一个基于LLM的检测流程，在识别这种偏见方面取得了87.37的宏观F1分数。识别出的最普遍的负面偏见形式是要求不合理的跨语言泛化。 AI

影响 凸显了人工智能研究评估中潜在的系统性偏见，影响了自然语言处理领域贡献的公平性和多样性。

排序理由 关于自然语言处理同行评审中偏见的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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研究发现非英语自然语言处理论文面临不公平的同行评审偏见

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关于自然语言处理同行评审中偏见的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ehsan Barkhordar, Abdulfattah Safa, Verena Blaschke, Erika Lombart, Marie-Catherine de Marneffe, G\"ozde G\"ul \c{S}ahin ·

    非英语论文是否得到公平评审？NLP同行评审中的语言偏见

    arXiv:2604.07119v2 Announce Type: replace Abstract: Peer review plays a central role in the NLP publication process, but is susceptible to various biases. Here, we study language-of-study (LoS) bias: the tendency for reviewers to evaluate a paper differently based on the language…

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