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Xingwu Sun

Xingwu Sun has 3 papers in our library, in 2018–2025, most of them on Language models & pretraining, Question answering; often with Jiaqi Zhu, Ruobing Xie, Shuaipeng Li.

3 papers in our library · 2018–2025

OpenAlex · ORCID · Wikidata · h-index 8 · 37 works · 346 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 98 times in our library · ⭐ Paper Stars ranking: #5014

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

🏢TencentShenzhen · China2021–2025 · 16 papersEmployment since 2021 · engineer, Machine Learning Platform Department🎓University of MacauMacao · China2022–2025 · 13 papers🏢MeituanBeijing · China2019–2021 · 6 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 5The 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 work

🧭 Topics 2Research subjects and languages the work is about.Question answering · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20251 paper20241 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Answer-focused and Position-aware Neural Question Generation (2018 · 255 citations)
    The paper tackles neural question generation: given a passage and a target answer span, the system must produce a fitting question.
  2. Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent (2024 · 25 citations)
    Most open-source LLMs use dense architectures, and the few public mixture-of-experts (MoE) models are relatively small.
  3. HMoE: Heterogeneous Mixture of Experts for Language Modeling (2025 · 11 citations)
    Standard Mixture of Experts (MoE) language models use experts of identical size, even though some tokens are harder to process than others.

 Co-authors here

11 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 98 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated

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