Large language models have shown impressive multilingual capabilities through pretraining on diverse corpora. While these models show strong reasoning abilities, their performance varies significantly across languages due to imbalanced training data distribution. Existing approaches using sample-level translation for extensive multilingual pretraining and cross-lingual tuning face scalability challenges and often fail to capture nuanced reasoning processes across languages. In this paper, we introduce AdaCoT (Adaptive Chain-of-Thought), a framework that enhances multilingual factual reasoning by dynamically routing thought processes in intermediary ``thinking languages'' before generating target-language responses. AdaCoT leverages a language-agnostic core and incorporates an adaptive, reward-based mechanism for selecting optimal reasoning pathways without requiring additional pretraining. Our comprehensive evaluation across multiple benchmarks demonstrates substantial improvements in both factual reasoning quality and cross-lingual consistency, with particularly strong performance gains in low-resource language settings. The results suggest that adaptive reasoning paths can effectively bridge the performance gap between high and low-resource languages while maintaining cultural and linguistic nuances.
View on arXiv@article{huang2025_2501.16154,
title={ AdaCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Chain-of-Thought },
author={ Xin Huang and Tarun Kumar Vangani and Zhengyuan Liu and Bowei Zou and Ai Ti Aw },
journal={arXiv preprint arXiv:2501.16154},
year={ 2025 }
}