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cs.LG2025
Why Do More Experts Fail? A Theoretical Analysis of Model Merging
Zijing Wang, Xingle Xu, Yongkang Liu +6
Model merging dramatically reduces storage and computational resources by combining multiple expert models into a single multi-task model. Although recent model merging methods hav…
cs.CL2025
HYPEROFA: Expanding LLM Vocabulary to New Languages via Hypernetwork-Based Embedding Initialization
Enes Özeren, Yihong Liu, Hinrich Schütze
Many pre-trained language models (PLMs) exhibit suboptimal performance on mid- and low-resource languages, largely due to limited exposure to these languages during pre-training. A…
cs.CL2025
Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu
Renhao Pei, Yihong Liu, Peiqin Lin +2
In-context machine translation (MT) with large language models (LLMs) is a promising approach for low-resource MT, as it can readily take advantage of linguistic resources such as…