Let Your LLM Think in English

In the context of multilingual AI systems, there is a significant challenge when users interact in various languages while the underlying tools expect inputs predominantly in English. This issue arises when language models comprehend user intent but fail due to language mismatches in parameter values, notably highlighted in multilingual benchmarks where English performs better than other languages. The proposed solution involves translating user queries into English for internal processing and then localizing responses back to the user's language. This approach ensures reliability and consistency, as recent research suggests that pre-translation reduces errors more effectively than post-processing fixes. While not perfect, especially for low-resource or culturally specific languages, this method is advocated as a pragmatic default for enterprise-level AI applications, emphasizing the importance of a stable English core and localized interfaces. This strategy aligns with maintaining a canonical content structure to ensure trustworthy AI outputs, suggesting that while user interfaces can be multilingual, the execution processes should remain stable, often by using English as the intermediary language.

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