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Shiqi Chen

Shiqi Chen has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP; often with Junxian He, I-Chun Chern, Jinghan Zhang.

3 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 7 · 26 works · 129 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 214 times in our library · ⭐ Paper Stars ranking: #5898

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

🎓Xiamen University of TechnologyXiamen · China2025–2026 · 3 paperscurrent🎓Sichuan University of Science and EngineeringZigong · China2025–2026 · 2 paperscurrent🎓Zhejiang UniversityHangzhou · China2021–2026 · 2 paperscurrent🎓City University of Hong KongHong Kong · China2023 · 3 papers🎓Nantong UniversityNantong · China2021 · 3 papers🎓Nankai UniversityTianjin · China2023–2025 · 2 papers🎓Nanjing University of Posts and TelecommunicationsNanjing · China2010–2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksSocial Sciences · 2 worksSociology and Political Science · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20261 paper20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. FacTool: Factuality Detection in Generative AI — A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios (2023 · 160 citations)
    Large language models such as ChatGPT write fluent long-form answers that may contain factual errors. Classic fact-checking setups rarely fit this case. They usually assume that the claim to check, or the evidence for it, is already given. LLM outputs instead come without either, and they are long, with no clear unit…
  2. FELM: Benchmarking Factuality Evaluation of Large Language Models (2023 · 99 citations)
    Detecting factual errors in LLM output is important, but the tools that do this detection also need to be measured.
  3. How Can Synthetic Data Improve Multilingual Language Model Pretraining? A Data Quality Perspective (2026)
    Multilingual LLMs perform poorly in low-resource languages. The authors argue the main gap is not token volume but how little world knowledge the available text contains. They trained 1.1B TinyLlama models from scratch on noisy MADLAD-400 web data in six languages, all with the same token budget. Scores on simpler…

 Co-authors here

12 of 14 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 214 times (of the 149,439 papers whose bibliographies we read); 61 citing papers with a page here →.

Updated

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