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arXiv · 2509.08105

MERLIN: Multi-Stage Curriculum Alignment for Multilingual Encoder-LLM Integration in Cross-Lingual Reasoning

Kosei Uemura·David Guzmán·Quang Phuoc Nguyen·Jesujoba Oluwadara Alabi·En-shiun Annie Lee·David Ifeoluwa Adelani
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Abstract

Large language models excel in English but still struggle with complex reasoning in many low-resource languages (LRLs). Existing encoder-plus-decoder methods such as LangBridge and MindMerger raise accuracy on mid and high-resource languages, yet they leave a large gap on LRLs. We present MERLIN, a two-stage model-stacking framework that applies a curriculum learning strategy -- from general bilingual bitext to task-specific data -- and adapts only a small set of DoRA weights. On the AfriMGSM benchmark MERLIN improves exact-match accuracy by +12.9 pp over MindMerger and outperforms GPT-4o-mini. It also yields consistent gains on MGSM and MSVAMP (+0.9 and +2.8 pp), demonstrating effectiveness across both low and high-resource settings.

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Kosei Uemura, David Guzmán, Quang Phuoc Nguyen, Jesujoba Oluwadara Alabi, En-shiun Annie Lee, David Ifeoluwa Adelani. 2025-09-09. MERLIN: Multi-Stage Curriculum Alignment for Multilingual Encoder-LLM Integration in Cross-Lingual Reasoning. https://arxiv.org/abs/2509.08105

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