MERLIN通过分阶段教学提升多语言大模型推理能力，尤其改善低资源语言表现。
MERLIN: Multi-Stage Curriculum Alignment for Multilingual Encoder-LLM Integration in Cross-Lingual Reasoning
- 分两阶段用泛化双语数据到任务特定数据进行渐进式对齐
- 在AfriMGSM上比MindMerger高12.9个百分点，超越GPT-4o-mini
- 仅调整少量DoRA权重，适合低资源语言场景的部署
大型语言模型在英语上表现优异，但在许多低资源语言（LRLs）中仍难以完成复杂推理。现有编码器-解码器方法如LangBridge和MindMerger虽提升了中高资源语言的准确率，但在低资源语言上仍有显著差距。我们提出MERLIN，一种两阶段模型堆叠框架，采用从通用双语语料到任务特定数据的课程学习策略，并仅微调少量DoRA权重。在AfriMGSM基准上，MERLIN相比MindMerger提升12.9个百分点的精确匹配准确率，优于GPT-4o-mini；在MGSM和MSVAMP上也分别取得+0.9和+2.8个百分点的稳定增益，证明其在低、高资源场景中的有效性。
原文摘要 · Abstract (English)
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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