arXiv:2605.25846v1 Announce Type: new Abstract: Endowing models with consistent multilingual performance can be achieved by mixing pre-training data, or post-training approaches such as language-specific model merging. In this work, we test whether merging can be applied to monolingually pre-trained models. We conduct a controlled study on the efficacy of mixed, merged, and monolingual pre-training setups. We find that while monolingual pre-training results in strong in-language performance, merging any combination of monolingual models leads to performance collapse due to interference. Our an
This paper addresses a fundamental limitation in AI model development, specifically regarding multilingual pre-training and the effectiveness of model merging, which is a current research frontier.
Understanding the inefficiencies of current model merging techniques for multilingual AI impacts the development cost and performance of global AI applications, potentially requiring more resource-intensive approaches.
The reported 'performance collapse due to interference' suggests that simple model merging strategies for multilinguality are ineffective, necessitating advanced or alternative methods for building robust multilingual AI models.
- · Large language model developers with dedicated multilingual pre-training strateg
- · Researchers focused on advanced model architecture and training techniques
- · Cloud providers supporting compute-intensive multilingual model development
- · Researchers relying on simple model merging for multilingual capabilities
- · Companies seeking cost-effective multilingual AI via post-training merging
- · Platforms without robust pre-training infrastructure for diverse languages
AI developers will need to re-evaluate their strategies for achieving multilingual performance, moving away from naive model merging.
This could lead to increased compute demands for pre-training truly multilingual models or a renewed focus on more sophisticated cross-lingual transfer techniques.
The complexity and cost of developing state-of-the-art multilingual AI may increase, potentially favoring larger institutions with greater resources for foundational research.
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