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arXiv · 2505.24409

When Language Shapes Thought: Cross-Lingual Transfer of Factual Knowledge in Question Answering

Eojin Kang·Juae Kim
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Abstract

Multilingual large language models (LLMs) offer promising opportunities for cross-lingual information access, yet their use of factual knowledge remains highly sensitive to the input language. Prior work has addressed this through English prompting and evaluation, assuming that English-based reasoning is universally beneficial. In this work, we challenge that assumption by exploring factual knowledge transfer from non-English to English through the lens of Language and Thought Theory. We introduce Language-to-Thought (L2T) prompting, which aligns the model's internal ''thinking'' language with the source of knowledge. Across three languages and four models, L2T consistently outperforms English-based reasoning, reversing the expected advantage of English prompts. Our code is available at https://github.com/GeomeunByeol/Language2Thought.

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Eojin Kang, Juae Kim. 2025-05-30. When Language Shapes Thought: Cross-Lingual Transfer of Factual Knowledge in Question Answering. https://doi.org/10.1145/3746252.3760807

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