The Unreasonable Effectiveness of Model Merging for Cross-Lingual Transfer in LLMs

• URL: http://arxiv.org/abs/2505.18356v1
• Date: Fri, 23 May 2025 20:28:31 GMT
• Title: The Unreasonable Effectiveness of Model Merging for Cross-Lingual Transfer in LLMs
• Authors: Lucas Bandarkar, Nanyun Peng,
• Abstract summary: Large language models (LLMs) still struggle across tasks outside of high-resource languages.<n>In this work, we investigate cross-lingual transfer to lower-resource languages where task-specific post-training data is scarce.
• Score: 54.59207567677249
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large language models (LLMs) still struggle across tasks outside of high-resource languages. In this work, we investigate cross-lingual transfer to lower-resource languages where task-specific post-training data is scarce. Building on prior work, we first validate that the subsets of model parameters that matter most for mathematical reasoning and multilingual capabilities are distinctly non-overlapping. To exploit this implicit separability between task and target language parameterization, we develop and analyze numerous modular frameworks to improve the composition of the two during fine-tuning. These methods generally employ freezing parameters or post hoc model merging to assign math and language improvement to different key parts of the LLM. In the absence of in-language math data, we demonstrate that the modular approaches successfully improve upon baselines across three languages, four models, and two fine-tuning paradigms (full and LoRA). Furthermore, we identify the most consistently successful modular method to be fine-tuning separate language and math experts and model merging via Layer-Swapping, somewhat surprisingly. We offer possible explanations for this result via recent works on the linearity of task vectors. We further explain this by empirically showing that reverting less useful fine-tuning updates after training often outperforms freezing them from the start.

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