Improving Training Efficiency and Reducing Maintenance Costs via Language Specific Model Merging

2026-01-22

AI Summary

This work addresses the high computational and maintenance costs associated with full retraining of multilingual large language models when adding or updating languages. To overcome this limitation, the authors propose a language-specific model merging strategy that efficiently integrates new languages or updated data by fusing dedicated language submodels, eliminating the need for full-scale retraining. As the first systematic study to evaluate multilingual model merging from an efficiency perspective, the approach maintains competitive model performance while reducing initial training time by up to 50% and cutting the cost of post-update re-merging for individual languages by over 60%. The effectiveness of the method is validated on both public and industrial datasets.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Fine-tuning a task-specific multilingual large language model (LLM) involves training the model on a multilingual dataset with examples in all the required languages. Updating one or more supported languages with additional data or adding support for a new language involves retraining the model, which can be computationally inefficient and creates a severe maintenance bottleneck. Recent research on merging multilingual multitask models has shown promise in terms of improved quality, but its computational and maintenance efficiency remains unstudied. In this work, we provide the first focused analysis of this merging strategy from an efficiency perspective, evaluating it across three independent tasks. We demonstrate significant efficiency gains while maintaining parity in terms of quality: this merging approach reduces the initial training time by up to 50\%. We also demonstrate that updating an individual language and re-merging as part of model maintenance reduces training costs by more than 60\%, compared to re-training the full multilingual model. We show this on both public and proprietary industry datasets confirming that the approach works well for industrial use cases in addition to academic settings already studied in previous work.

Problem

• multilingual LLM
• model maintenance
• training efficiency
• language updating
• computational cost

Innovation

• model merging
• multilingual LLM
• training efficiency
• maintenance cost reduction
• language-specific fine-tuning