Smoothie-Qwen: Post-Hoc Smoothing to Reduce Language Bias in Multilingual LLMs

SeungWon Ji; Jungyup Lee; Jemin Kim; Sang Park; SeungJae Lee

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arXiv · 2507.05686

Smoothie-Qwen: Post-Hoc Smoothing to Reduce Language Bias in Multilingual LLMs

SeungWon Ji·Jungyup Lee·Jemin Kim·Sang Park·SeungJae Lee

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Abstract

Multilingual large language models (LLMs) often exhibit language confusion, a tendency to generate responses in a dominant language irrespective of the prompt's language. To address this, we propose Smoothie-Qwen, a lightweight, post-hoc method that mitigates language bias without retraining. This technique selectively adjusts token-level output probabilities to effectively suppress undesired language generation. Applied to the Qwen model, our method reduces unintended Chinese output by over 95% while preserving task accuracy on multilingual benchmarks. This work provides a practical and efficient solution for enhancing the language controllability of LLMs, making them more reliable for global applications.

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SeungWon Ji, Jungyup Lee, Jemin Kim, Sang Park, SeungJae Lee. 2025-07-08. Smoothie-Qwen: Post-Hoc Smoothing to Reduce Language Bias in Multilingual LLMs. https://arxiv.org/abs/2507.05686

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