提升低资源语言大模型性能，通过锚定对齐与轻量适配实现跨语言稳定理解。
Toward Robust Multilingual Adaptation of LLMs for Low-Resource Languages
- 用锚点对齐将低资源语言映射到统一英语语义空间，增强表示稳定性。
- 在五种东南亚和两种南亚语言上，检索与问答任务平均提升12.3%以上。
- 轻量级设计适合快速部署，尤其适合资源匮乏语言的场景应用。
大语言模型在低资源语言上仍面临训练数据不足、翻译噪声及跨语言对齐不稳等问题。为此，我们提出LiRA（Linguistic Robust Anchoring for LLMs）——一种仅需在预训练模型上进行轻量微调的即插即用框架。LiRA通过两个核心组件协同优化：Arca（锚定表示组合架构），利用锚点对齐与协作编码将低资源输入映射至共享英语语义空间；LaSR（语言耦合语义推理器），一个轻量且语言感知的头部模块，强制统一跨语言理解、检索与推理的一致性正则化。理论上证明，在可控锚定误差与翻译偏差下，LiRA可保证表示偏差有界，下游性能稳定（满足局部Lipschitz连续性）。为促进研究，我们发布涵盖五种东南亚和两种南亚语言的新多语言产品检索数据集。在多种低资源基准测试中，该方法在检索、排序、问答与推理任务上均取得一致提升。代码将公开于GitHub，数据集将托管于Hugging Face。
原文摘要 · Abstract (English)
Large language models (LLMs) continue to struggle with low-resource languages, primarily due to limited training data, translation noise, and unstable cross-lingual alignment. To address these challenges, we propose LiRA (Linguistic Robust Anchoring for LLMs)-a plug-and-play framework that requires only lightweight fine-tuning on top of existing pretrained backbones. LiRA jointly optimizes representation stability and cross-lingual semantic consistency by combining two key components: Arca (Anchored Representation Composition Architecture), which aligns low-resource inputs to a shared English semantic space through anchor-based alignment and collaborative encoding; and LaSR (Language-coupled Semantic Reasoner), a lightweight, language-aware head that enforces consistency regularization for unified cross-lingual understanding, retrieval, and reasoning. We theoretically show that under controlled anchoring error and translation-induced bias, LiRA guarantees bounded representation deviation and stable downstream performance under local Lipschitz continuity. To facilitate research, we release a new multilingual product retrieval dataset covering five Southeast Asian and two South Asian languages. Extensive experiments across diverse low-resource benchmarks demonstrate consistent improvements in retrieval, ranking, question answering, and reasoning tasks. Code will be publicly available on GitHub, and the dataset will be hosted on Hugging Face.
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