Completely Modular Fine-tuning for Dynamic Language Adaptation (2026.findings-eacl)
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| Challenge: | Existing studies on multilingual fine-tuning with a fixed set of languages lack dynamic adaptability to new languages. |
| Approach: | They propose a modular fine-tuning pipeline that enables dynamic language adaptation for LLMs by first training English-centric adapters for each language separately and then merging them for arbitrary-direction translation. |
| Outcome: | The proposed pipeline achieves 86% performance over traditional fine-tuning on four languages, while training only 0.1% parameters and relying on English as a bridge language without catastrophic forgetting. |
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| Challenge: | Traditionally, success in multilingual machine translation depends on large volume, diverse directions, and high quality of training data. |
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Yangfan Ye, Xiaocheng Feng, Zekun Yuan, Xiachong Feng, Libo Qin, Lei Huang, Weitao Ma, Yichong Huang, Zhirui Zhang, Yunfei Lu, Xiaohui Yan, Duyu Tang, Dandan Tu, Bing Qin
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| Challenge: | Large language models (LLMs) have demonstrated strong performance even with limited parallel data. |
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| Challenge: | Existing models that target a single language are not seen during finetuning, but are able to respond in multiple languages once deployed in downstream applications. |
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| Challenge: | Adapter tuning is an efficient alternative to fine-tuning in NLP . a multilingual model could be outperformed by its bilingual counterparts . |
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| Challenge: | Recent studies have shown that fine-tuning large language models improves their translations, but it is unclear what is the impact on desirable LLM behaviors that are not present in neural machine translation models. |
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| Challenge: | Large language models (LLMs) are mainly trained on English data and struggle with low-resource languages. |
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