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This paper investigates whether forcing LLMs to reason in the same language as the query and retrieved documents improves monolingual non-English RAG performance. Using a German RAG testbed built over the fictional world of The Dark Eye, the authors find that aligning the reasoning language with the evidence helps (German reasoning beats French despite the model's stronger French proficiency), but native unconstrained English reasoning still matches the aligned setting, highlighting the need for truly multilingual reasoning models.
该论文研究了在单语检索增强生成（RAG）中推理语言与查询/检索文档语言对齐的作用，构建了一个基于虚构桌游世界《The DarkEye》的德语 RAG 问答测试集，发现对齐推理语言能提升表现，但仅能达到模型原生英语推理的水平。
This paper proposes HomoEnsNER, a homogeneous ensemble of five GujaratiBERT models for Gujarati named entity recognition, and shows it outperforms heterogeneous alternatives that rely on architectural diversity, achieving state-of-the-art F1 on the Naamapadam test split.