Language-Aware Token Boosting: LLM Language Confusion Reduction Without Tuning

• URL: http://arxiv.org/abs/2606.08994v1
• Date: Mon, 08 Jun 2026 03:47:07 GMT
• Title: Language-Aware Token Boosting: LLM Language Confusion Reduction Without Tuning
• Abstract summary: Large language models (LLMs) sometimes exhibit language confusion when generating non-English text.<n>We propose a tuning-free paradigm for reducing language confusion.<n>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.
• Score: 0.19116784879310025
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs [32.94544776067079]
  ASR transcripts often contain disfluencies, such as fillers, repetitions, and false starts.<n>Most existing approaches rely on classical models that focus on identifying disfluent tokens for removal.<n>We propose a multilingual correction pipeline where a sequence tagger first marks disfluent tokens, and these signals guide instruction fine-tuning of an LLM to rewrite transcripts into fluent text.
  arXiv  Detail & Related papers  (2026-05-12T15:11:36Z)
• Language Confusion Gate: Language-Aware Decoding Through Model Self-Distillation [50.93756215410832]
  This paper introduces the Language Confusion Gate (LCG), a lightweight, plug-in solution that filters tokens during decoding.<n>The LCG is trained using norm-adjusted self-distillation to predict appropriate language families and apply masking only when needed.
  arXiv  Detail & Related papers  (2025-10-20T14:02:37Z)
• Smoothie-Qwen: Post-Hoc Smoothing to Reduce Language Bias in Multilingual LLMs [4.881694369042022]
  Smoothie-Qwen is a lightweight, post-hoc method that mitigates language bias without retraining.<n>applied to the Qwen model, our method reduces unintended Chinese output by over 95%.
  arXiv  Detail & Related papers  (2025-07-08T05:30:51Z)
• Retrofitting Large Language Models with Dynamic Tokenization [3.608780819053423]
  Current language models (LMs) use a fixed, static subword tokenizer.<n>This default choice typically results in degraded efficiency and language capabilities, especially in languages other than English.<n>We propose retrofitting LMs with dynamic tokenization: a way to dynamically decide on token boundaries based on the input text.
  arXiv  Detail & Related papers  (2024-11-27T17:51:58Z)
• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts.<n>We find that Llama Instruct and Mistral models exhibit high degrees of language confusion.<n>We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment [50.27950279695363]
  The transfer performance is often hindered when a low-resource target language is written in a different script than the high-resource source language. Inspired by recent work that uses transliteration to address this problem, our paper proposes a transliteration-based post-pretraining alignment (PPA) method.
  arXiv  Detail & Related papers  (2024-06-28T08:59:24Z)
• InstructAlign: High-and-Low Resource Language Alignment via Continual Crosslingual Instruction Tuning [66.31509106146605]
  Large language models (LLMs) that are tuned with instructions have demonstrated remarkable capabilities in various tasks and languages. However, their ability to generalize to underrepresented languages is limited due to the scarcity of available data. We propose InstructAlign which uses continual crosslingual instruction tuning to enable LLMs to align new unseen languages with previously learned high-resource languages.
  arXiv  Detail & Related papers  (2023-05-23T02:51:34Z)
• Code-Switching without Switching: Language Agnostic End-to-End Speech Translation [68.8204255655161]
  We treat speech recognition and translation as one unified end-to-end speech translation problem. By training LAST with both input languages, we decode speech into one target language, regardless of the input language.
  arXiv  Detail & Related papers  (2022-10-04T10:34:25Z)
• On the Importance of Word Order Information in Cross-lingual Sequence Labeling [80.65425412067464]
  Cross-lingual models that fit into the word order of the source language might fail to handle target languages. We investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages.
  arXiv  Detail & Related papers  (2020-01-30T03:35:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.