arXiv:2606.05613v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has established cross-lingual versatility as a defining feature of modern systems. However, fine-tuning these models frequently induces negative interference across languages. To address this, we reformulate multilingual fine-tuning as a multi-objective optimization (MOO) problem. Specifically, we introduce Bucket-Level MOO, a scalable distributed framework that applies gradient-based MOO algorithms locally on parameter buckets. This enables conflict-aware updates without the prohibitive communi
The proliferation of LLMs and their application across diverse linguistic contexts necessitates solutions to inherent multilingual fine-tuning challenges, such as negative interference.
Improving multilingual performance in LLMs expands their global applicability and reduces the need for language-specific models, thereby increasing efficiency and reducing costs.
Multilingual LLM fine-tuning can become more robust and less prone to performance degradation across languages, enabling broader and more effective deployment.
- · Global AI developers
- · Multinational corporations
- · Non-English speaking markets
- · Cloud AI providers
- · Niche language-specific AI model developers (if not integrated)
LLMs can be fine-tuned more effectively for multiple languages simultaneously, leading to better cross-lingual performance.
This improved versatility could accelerate the adoption of advanced AI in non-English speaking regions and open new markets for AI applications.
Generalized multilingual LLMs could reduce linguistic barriers to information access and technological integration globally, potentially influencing knowledge distribution and economic development.
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Read at arXiv cs.AI