On the Limits of Model Merging for Multilinguality in Pre-Training

2026-06-26 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A study published in the Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026) investigates the applicability of model merging for achieving multilinguality during pre-training. Researchers Seth Aycock, Fedor Vitiugin, Aleksandr Umnov, Christof Monz, and Khalil Sima'an conducted a controlled study comparing mixed, merged, and monolingual pre-training setups. They found that while monolingual pre-training yields strong in-language performance, merging any combination of monolingual models results in a performance collapse due to interference. The analysis suggests that representational similarity is a prerequisite for successful model merging, concluding that the technique's flexibility observed in fine-tuning does not trivially extend to language-specific pre-training.

Key takeaway

For AI Scientists or ML Engineers exploring multilingual model development, understand that directly merging monolingually pre-trained models is unlikely to yield effective multilingual performance. Your efforts should instead focus on data mixing during pre-training or ensuring high representational similarity if considering merging. Do not assume model merging's success in fine-tuning translates to pre-training.

Key insights

Merging monolingually pre-trained models for multilinguality causes performance collapse due to interference, requiring representational similarity.

Principles

• Representational similarity is crucial for model merging.
• Monolingual pre-training yields strong in-language performance.
• Merging flexibility in fine-tuning differs from pre-training.

Method

A controlled study compared mixed, merged, and monolingual pre-training setups to evaluate multilinguality efficacy.

In practice

• Avoid merging disparate monolingual models for pre-training.
• Consider data mixing for multilingual pre-training.

Topics

• Model Merging
• Multilingual Models
• Pre-training
• Monolingual Models
• Neural Network Interference
• Representational Similarity

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.