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Jingqi Tong

Jingqi Tong has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Reasoning; often with Chenglin Li, Conghui He, Haitao Li.

3 papers in our library · 2024–2026

OpenAlex · h-index 3 · 32 works · 54 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 58 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6914

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Fudan UniversityShanghai · China2024–2026 · 15 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 workSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 1 work

 Works by year

2020s

20262 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-Training (2024 · 85 citations)
    Training sparse Mixture-of-Experts (MoE) language models from scratch needs a lot of data and can be unstable.
  2. VideoPro: Adaptive Program Reasoning for Long Video Understanding (2026 · 6 citations)
    Long videos are hard for VideoLLMs because the visual cues needed to answer a question are few and scattered across a long timeline.
  3. LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models (2026 · 3 citations)
    Fixed public test sets can leak into training corpora and be overfitted, so leaderboard scores may overstate real LLM ability.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-TrainingConference on Empirical Methods in Natural Language Processing (EMNLP)Tong Zhu, Xiaoye Qu, Daize Dong, et al.2024 · 85 citations
  2. VideoPro: Adaptive Program Reasoning for Long Video UnderstandingAnnual Meeting of the Association for Computational Linguistics (ACL)Chenglin Li, Feng Han, Yikun Wang, et al.2026 · 6 citations
  3. LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Ming Zhang, Yujiong Shen, Jingyi Deng, et al.2026 · 3 citations
  4. FARS: A Fully Automated Research System Deployed at Scale ↗arXiv:2606.31651 · no summary yetQiong Tang et al.2026 · 1 citation

 Co-authors here

12 of 16 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 58 times (of the 149,439 papers whose bibliographies we read); 15 citing papers with a page here →.

Updated

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