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Jiahao Ying

Jiahao Ying has 2 papers in our library, in 2025, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Evaluation & benchmarks; often with Wenxuan Zhang, Yiran Zhao, Yu Rong.

2 papers in our library · 2025

OpenAlex · h-index 3 · 13 works · 40 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 93 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9215

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

🎓Singapore Management UniversitySingapore · Singapore2024–2026 · 8 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksMultilingual & cross-lingual NLP · 2 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Disentangling Language and Culture for Evaluating Multilingual Large Language Models (2025 · 10 citations)
    Multilingual LLM evaluation usually relies on one of two approaches. The first translates English-centric benchmarks, such as MMLU into MMMLU, which keeps the content anchored in Anglophone culture. The second uses native, culture-specific sets such as M3Exam and BLEnD, which tie each language to its own culture.…
  2. Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers (2025 · 8 citations)
    Open multilingual LLMs tend to favor languages with abundant web text, such as French or German. Populous but data-poor languages like Hindi, Bengali or Urdu remain underserved. Babel, from Alibaba's DAMO Academy, targets the 25 languages with the most speakers, which together cover roughly 7 billion people.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Benchmarking Foundation Models with Language-Model-as-an-Examiner ↗arXiv:2306.04181 · no summary yetYushi Bai et al.2023 · 106 citations
  2. Disentangling Language and Culture for Evaluating Multilingual Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jiahao Ying, Wei Tang, Yiran Zhao, et al.2025 · 10 citations
  3. Babel: Open Multilingual Large Language Models Serving Over 90% of Global SpeakersarXiv:2503.00865Yiran Zhao, Chaoqun Liu, Yue Deng, et al.2025 · 8 citations

 Co-authors here

10 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 93 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

Updated

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