40 citations · 58 across the 19 of their papers we have counts for
Showing 2024 · cs.CLShow all
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cs.CL2024★ 3 cited
Unlocking the Potential of Model Merging for Low-Resource Languages
Mingxu Tao, Chen Zhang, Quzhe Huang +4
Adapting large language models (LLMs) to new languages typically involves continual pre-training (CT) followed by supervised fine-tuning (SFT). However, this CT-then-SFT approach s…
cs.CL2024★ 2 cited
Can Perplexity Reflect Large Language Model's Ability in Long Text Understanding?
Yutong Hu, Quzhe Huang, Mingxu Tao +2
Recent studies have shown that Large Language Models (LLMs) have the potential to process extremely long text. Many works only evaluate LLMs' long-text processing ability on the la…
cs.CL2024★ 2 cited
Teaching Large Language Models an Unseen Language on the Fly
Chen Zhang, Xiao Liu, Jiuheng Lin +1
Existing large language models struggle to support numerous low-resource languages, particularly the extremely low-resource ones, for which there is minimal training data available…