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.
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
Affiliations
Fields, topics and concepts
📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksArts and Humanities · 4 worksComputer Science · 4 worksLanguage and Linguistics · 4 works
🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3 worksEnglish · 1 workMultilingual & cross-lingual NLP · 1 work
Works by year
2020s
Papers
- 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. - 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. - 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: - 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
Venues
- Conference of the European Chapter of the Association for Computational Linguistics (EACL): EACL 2026
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
- Frontiers in artificial intelligence and applications: Frontiers in artificial intelligence and applications 2024
- Workshop on Language Models for Low-Resource Languages: Workshop on Language Models for Low-Resource Languages 2026
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