🧑‍🔬 Can Ren

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Large-Scale Diverse Synthesis for Mid-Training (2026)
   Mid-training large language models often relies on synthetic question–answer data. However, existing generators mostly reformat source text into simple, uniform QA pairs, which limits both volume and variety. The authors present BoostQA, a pipeline that first diagnoses what a pre-trained model lacks and then…
2. LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge Points (2026)
   Training LLMs is increasingly limited by a shortage of diverse, high-quality data, especially across many academic disciplines.

🤝 Co-authors here

6 co-authors with a page, the most papers together first.

• Chengying Tu ×2
• Hongfei Yan ×2
• Jingang Wang ×2
• Rongxiang Weng ×2
• Xuemiao Zhang ×2
• Xunliang Cai ×2

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026

Updated 2 November 2026

This page in Markdown → https://md.research.elamur.ai/free/en/authors/can-ren/