x1 models adaptively select an advantageous language for reasoning per instance, yielding gains on multilingual math and cultural tasks while showing that scaling does not erase culture-language advantages.
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AdaMCoT uses dynamic routing of chain-of-thought reasoning in intermediary languages with a reward-based selector to improve cross-lingual factual consistency in LLMs.
citing papers explorer
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x1: Learning to Think Adaptively Across Languages and Cultures
x1 models adaptively select an advantageous language for reasoning per instance, yielding gains on multilingual math and cultural tasks while showing that scaling does not erase culture-language advantages.
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AdaMCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Multilingual Chain-of-Thought
AdaMCoT uses dynamic routing of chain-of-thought reasoning in intermediary languages with a reward-based selector to improve cross-lingual factual consistency in LLMs.