Merge and Conquer: Instructing Multilingual Models by Adding Target Language Weights
Large Language Models (LLMs) remain heavily centered on English, with limited performance in low-resource languages. Existing adaptation approaches, such as continual pre-training, demand significant computational resources. In the case of instructed models, high-quality instruction data is also required, both of which are often inaccessible for low-resource language communities. Under these constraints, model merging offers a lightweight alternative, but its potential in low-resource contexts has not been systematically explored. In this work, we explore whether it is possible to transfer language knowledge to an instruction-tuned LLM by merging it with a language-specific base model, th...
| Authors | Eneko Valero, Maria Ribalta i Albado, Oscar Sainz et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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