AdaMCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Multilingual Chain-of-Thought

Understand

Large language models (LLMs) have shown impressive multilingual capabilities through pretraining on diverse corpora.

• Although these models show strong reasoning abilities, their performance varies significantly between languages due to the imbalanced distribution of training data.
• 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 AdaMCOT (Adaptive Multilingual Chain-of-Thought), a framework that enhances multilingual factual reasoning by dynamically routing thought processes in intermediary "thinking languages" before generating target-language responses.