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

• URL: http://arxiv.org/abs/2501.16154v1
• Date: Mon, 27 Jan 2025 15:48:57 GMT
• Title: AdaCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Chain-of-Thought
• Authors: Xin Huang, Tarun Kumar Vangani, Zhengyuan Liu, Bowei Zou, Ai Ti Aw,
• Abstract summary: We introduce AdaCoT (Adaptive Chain-of-Thought), a framework that enhances multilingual reasoning. AdaCoT dynamically routing thought processes through intermediary "thinking languages" before generating target-language responses.
• Score: 19.692743208974296
• License:
• Abstract: Large language models (LLMs) 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 uneven training data distribution. Existing approaches using machine translation, and 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 reasoning by dynamically routing thought processes through 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.

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