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Khanh-Tung Tran

Khanh-Tung Tran has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, English; often with Barry O'Sullivan, Hoang D. Nguyen.

4 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 4 · 15 works · 43 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 15 times in our library · ⭐ Paper Stars ranking: #9257

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

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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. UCCIX: Irish-eXcellence Large Language Model (2024 · 14 citations)
    Irish is a UNESCO "definitely endangered" language with only a few hundred million tokens of available text, far below what scaling laws suggest for training an LLM.
  2. Qomhrá: A Bilingual Irish and English Large Language Model (2026 · 2 citations)
    Irish has very little language technology support among official EU languages, and permissively licensed labelled data for instruction tuning and alignment is especially scarce.
  3. Disentangling Continued Pre-Training: Attention-Driven Routing and Semantic Hub Preservation in Language Adaptation (2026 · 1 citation)
    The paper examines what happens inside an LLM during Continued Pre-Training (CPT), the process that teaches it a second language. The authors trace the full training trajectory instead of only the final checkpoint. They use English as the base language and three targets: Irish, Basque, and Chinese. The models are:
  4. LaCoMSA: Language-Consistency Multilingual Self-Alignment with Latent Representation Rewarding (2026)
    Multilingual LLMs tend to answer worse in lower-resource languages than in English. They also often switch into English even when prompted in another language. Earlier preference-optimization fixes such as MAPO, LIDR and ICR depend on outside supervision, such as translation systems or English-trained reward models.

 Co-authors here

2 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 15 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated

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