Toward Robust Multilingual Adaptation of LLMs for Low-Resource Languages

Abstract

Large language models (LLMs) continue to struggle with low-resource languages due to limited training data, translation noise, and unstable cross-lingual alignment. We propose LiRA (Linguistic Robust Anchoring for LLMs), a plug-and-play framework that improves multilingual adaptation through fine-tuning on existing pretrained backbones. LiRA combines two components: Arca, which aligns low-resource inputs to a shared English semantic space through anchor-based alignment and collaborative encoding, and LaSR, a language-aware head that promotes cross-lingual consistency for retrieval, ranking, and reasoning. We provide a theoretical analysis showing that, under bounded anchoring error and translation-induced bias, LiRA yields bounded representation deviation and stable downstream behavior under local Lipschitz continuity. We further introduce a multilingual product retrieval dataset covering five Southeast Asian and two South Asian languages. Experiments on retrieval, ranking, question answering, and reasoning benchmarks show that LiRA consistently improves strong multilingual and LLM-based baselines. Code and data will be released publicly.