arXiv:2605.26735v1 Announce Type: new Abstract: Recent reasoning Large Language Models produce a chain-of-thought (CoT) predominantly in English, even when prompted in non-English languages. Prior work suggests that forcing the CoT to remain in the input language (\emph{native reasoning}) substantially degrades performance relative to allowing the model to reason in English before answering in the input language (\emph{English-pivoted reasoning}). However, most studies of this native reasoning gap rely on inference-time interventions or limited native-language training data. We revisit this co
This research addresses a fundamental limitation in current multilingual LLMs, which predominantly reason in English despite being prompted in other languages, a gap that becomes more critical as AI scales globally.
A strategic reader should care because this research impacts the fundamental efficiency and performance of LLMs in diverse linguistic contexts, critical for global AI adoption and reducing English-centrism.
The understanding of multilingual reasoning in LLMs is shifting, potentially leading to models that can truly reason natively in non-English languages without performance degradation.
- · Non-English speaking markets
- · Multilingual AI research
- · AI localization services
- · English-centric AI frameworks
- · Companies relying on English-pivoted reasoning
Improved performance of LLMs in non-English languages through more effective native reasoning.
Increased global adoption and accessibility of advanced AI, especially in emerging markets.
Enhanced cultural relevance and reduced bias in AI outputs, fostering more equitable and effective human-AI interaction worldwide.
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Read at arXiv cs.CL