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Dinh Viet Sang

Dinh Viet Sang has 3 papers in our library, in 2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Interpretability; often with Linh Ngo Van, Minh Chu Xuan, Trung Le.

3 papers in our library · 2026

OpenAlex · ORCID · h-index 12 · 92 works · 777 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 13 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #10643

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

🎓Hanoi University of Science and TechnologyHanoi · Vietnam2014–2026 · 72 paperscurrent🎓Trường Đại học Khoa học và Công nghệ Hà NộiHanoi · Vietnam2014–2026 · 64 paperscurrent🎓National University of SingaporeSingapore · Singapore2023–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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 worksGeneral Social Sciences · 2 worksSocial Sciences · 2 works

🧭 Topics 3Research subjects and languages the work is about.Interpretability · 1 workLanguage models & pretraining · 1 workMultilingual & cross-lingual NLP · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Topic modeling · 2 works

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. FAID: Fine-grained AI-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive Learning (2026 · 3 citations)
    Detecting machine-written text is harder when humans and LLMs co-write, and most detectors only output a human vs.
  2. Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models (2026 · 1 citation)
    Dynamic topic models track how themes in timestamped collections appear, change and fade. The authors identify three weaknesses in current approaches. Encoders learn only from bag-of-words counts and ignore knowledge in pre-trained language models. Topics in neighbouring time slices are usually linked one-to-one, so…
  3. LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models (2026)
    Cross-lingual topic models try to find themes shared across languages. They depend on limited bilingual resources such as seed dictionaries, document embeddings or parallel corpora, so paired topics often drift apart in meaning. Earlier LLM-assisted methods either query the model for every document, which is costly…

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. Improving Vietnamese-English Cross-Lingual Retrieval for Legal and General Domains ↗Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers) · no summary yetToan Ngoc Nguyen et al.2025 · 14 citations
  2. FAID: Fine-grained AI-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive LearningConference of the European Chapter of the Association for Computational Linguistics (EACL)Minh Ngoc Ta, Dong Cao Van, Duc-Anh Hoang, et al.2026 · 3 citations
  3. Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic ModelsFindings of the Association for Computational LinguisticsThanh Vinh Nguyen, Ngo Van Dong, Minh Chu Xuan, et al.2026 · 1 citation
  4. LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Minh Chu Xuan, Tien-Phat Nguyen, Linh Ngo Van, et al.2026

 Co-authors here

8 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 13 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

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