1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CL2025
LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge Points
Xuemiao Zhang, Can Ren, Chengying Tu +4
The advancement of large language models (LLMs) struggles with the scarcity of high-quality, diverse training data. To address this limitation, we propose LinkSyn, a novel knowledg…
cs.CL2025
Large-Scale Diverse Synthesis for Mid-Training
Xuemiao Zhang, Chengying Tu, Can Ren +4
The scarcity of high-quality, knowledge-intensive training data hinders the development of large language models (LLMs), as traditional corpora provide limited information. Previou…
cs.AI2025★ 1 cited
OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling
Hongliang Lu, Zhonglin Xie, Yaoyu Wu +3
Despite the rapid development of large language models (LLMs), a fundamental challenge persists: the lack of high-quality optimization modeling datasets hampers LLMs' robust modeli…