How Alibaba Tackles Low-Resource Language Data for Multilingual LLMs

Alibaba International’s senior data science expert explains a systematic five‑strategy solution—data acquisition, augmentation, quality optimization, engineering pipeline, and evaluation loop—to overcome data scarcity, high annotation cost, and processing challenges for low‑resource languages in multilingual large language models.

AIModel Evaluationdata engineering

0 likes · 13 min read

How Alibaba Tackles Low-Resource Language Data for Multilingual LLMs

DataFunSummit

Jul 13, 2025 · Artificial Intelligence

How Alibaba Tackles Low-Resource Language Data for Multilingual LLMs

In this interview, Alibaba International’s senior data‑science expert Li Haijun explains the challenges of low‑resource languages for multilingual large models and details a five‑step data‑collection, augmentation, quality‑optimization, engineering, and evaluation framework that powers their cross‑border e‑commerce AI applications.

AIlarge language modelslow-resource languages

0 likes · 12 min read

vivo Internet Technology

Feb 12, 2025 · Artificial Intelligence

Bidirectional Optimization of NLLB-200 and ChatGPT for Low-Resource Language Translation

The paper proposes a bidirectional optimization framework that fine‑tunes the low‑resource NLLB‑200 translation model with LoRA using data generated by ChatGPT, while also translating low‑resource prompts with NLLB before feeding them to LLMs, thereby improving multilingual translation quality yet requiring careful validation of noisy synthetic data.

Fine-tuningLLMLoRA

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Bidirectional Optimization of NLLB-200 and ChatGPT for Low-Resource Language Translation