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This research introduces a tuning-free method to reduce language confusion in LLMs by applying targeted token perturbations, offering a lightweight alternative to fine-tuning for improving multilingual alignment. The approach dynamically adjusts these perturbations based on model confidence while preserving summarization quality, making it immediately applicable for developers seeking to enhance non-English generation without additional training costs.
Language-Aware Token Boosting: LLM Language Confusion Reduction Without Tuning
arXiv:2606.08994v1 Announce Type: new Abstract: Large language models (LLMs) sometimes exhibit language confusion when generating non-English text. Existing approaches typically rely on fine-tuning to mitigate this issue. In contrast, we propose a tuning-free paradigm for reducing language confusion. Within this paradigm, we introduce two methods: Language-Aware Token Boosting (LATB), which applies targeted perturbations to tokens associated with the desired language, and Adaptive Language-Aware Token Boosting (Adaptive-LATB), which dynamically adjusts these perturbations based on the model's confidence in the intended language. Experiments demonstrate that our methods effectively improve multilingual alignment by reducing language confusion, while maintain the summarization quality without requiring any additional fine-tuning. Our code is publicly available. https://github.com/scbdatax/genai-datax-language-aware-token-boosting.
Abstract:Large language models (LLMs) sometimes exhibit language confusion when generating non-English text. Existing approaches typically rely on fine-tuning to mitigate this issue. In contrast, we propose a tuning-free paradigm for reducing language confusion. Within this paradigm, we introduce two methods: Language-Aware Token Boosting (LATB), which applies targeted perturbations to tokens associated with the desired language, and Adaptive Language-Aware Token Boosting (Adaptive-LATB), which dynamically adjusts these perturbations based on the model's confidence in the intended language. Experiments demonstrate that our methods effectively improve multilingual alignment by reducing language confusion, while maintain the summarization quality without requiring any additional fine-tuning. Our code is publicly available. this https URL.
Computation and Language (cs.CL)
arXiv:2606.08994 [cs.CL]
(or arXiv:2606.08994v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.08994 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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