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cs.CL2025
From Unaligned to Aligned: Scaling Multilingual LLMs with Multi-Way Parallel Corpora
Yingli Shen, Wen Lai, Shuo Wang +4
Continued pretraining and instruction tuning on large-scale multilingual data have proven to be effective in scaling large language models (LLMs) to low-resource languages. However…
cs.CL2025
DCAD-2000: A Multilingual Dataset across 2000+ Languages with Data Cleaning as Anomaly Detection
Yingli Shen, Wen Lai, Shuo Wang +4
The rapid development of multilingual large language models (LLMs) highlights the need for high-quality, diverse, and well-curated multilingual datasets. In this paper, we introduc…
cs.CL2025
Joint Localization and Activation Editing for Low-Resource Fine-Tuning
Wen Lai, Alexander Fraser, Ivan Titov
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, are commonly used to adapt LLMs. However, the effectiveness of standard PEFT methods is limited in low-resource scenar…