MBZUAI · Significant research
MBZUAI researchers created Bactrian-X, a new dataset to improve LLM instruction following in low-resource languages. The dataset leverages instruction tuning, pairing instructions in various languages with expected responses. Bactrian-X builds upon existing open-source instruction tuning models. Why it matters: This work aims to democratize access to LLMs by enabling users to interact with them in their native languages, even when English proficiency is limited.
LLM · instruction tuning · low-resource languages · MBZUAI · Bactrian-X
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arXiv ·
MBZUAI releases Bactrian-X, a multilingual parallel dataset of 3.4 million instruction-response pairs across 52 languages. They trained low-rank adaptation (LoRA) adapters using this dataset, creating lightweight, replaceable components for large language models. Experiments show the LoRA-based models outperform vanilla and existing instruction-tuned models in multilingual settings.
arXiv ·
Researchers have introduced LlamaLens, a specialized multilingual LLM designed for analyzing news and social media content. The model addresses domain specificity and multilinguality, with a focus on news and social media in Arabic, English, and Hindi. LlamaLens was evaluated on 18 tasks represented by 52 datasets, outperforming the state-of-the-art on 23 testing sets. Why it matters: This work contributes a valuable resource for multilingual NLP research, particularly in the context of analyzing news and social media content across diverse languages.
arXiv ·
Researchers introduce a benchmark to evaluate the factual recall and knowledge transferability of multilingual language models across 13 languages. The study reveals that language models often fail to transfer knowledge between languages, even when they possess the correct information in one language. The benchmark and evaluation framework are released to drive future research in multilingual knowledge transfer.
arXiv ·
The Hala technical report introduces a family of Arabic-centric instruction and translation models developed using a translate-and-tune pipeline. A strong Arabic-English teacher model is compressed to FP8 and used to create bilingual supervision data. The LFM2-1.2B model is fine-tuned on this data and used to translate English instruction sets into Arabic, creating a million-scale corpus. Why it matters: The release of models, data, evaluation tools, and recipes will accelerate research and development in Arabic NLP, providing valuable resources for the community.