1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CL2026
Bridging Linguistic Gaps: Cross-Lingual Mapping in Pre-Training and Dataset for Enhanced Multilingual LLM Performance
Weihua Zheng, Chang Liu, Zhengyuan Liu +5
Multilingual Large Language Models (LLMs) struggle with cross-lingual tasks due to data imbalances between high-resource and low-resource languages, as well as monolingual bias in…
cs.CL2025★ 1 cited
Long Is More Important Than Difficult for Training Reasoning Models
Si Shen, Fei Huang, Zhixiao Zhao +3
Difficult problems, which often result in long reasoning traces, are widely recognized as key factors for enhancing the performance of reasoning models. However, such high-challeng…