collaborators

7 papers

cs.CL2026

VIVID: A Culturally Grounded Benchmark Exposing the Figurative Language Gap in Vietnamese NLP

Tu Tran Do, Nhat Ngoc Nguyen, Khanh-Tung Tran +3

We present VIVID (Vietnamese Idioms for Validation and Interpretation Depth), the first systematic benchmark for evaluating culturally grounded figurative language understanding in…

cs.AI2026

ToolBrain: A Flexible Reinforcement Learning Framework for Agentic Tools

Quy Minh Le, Minh Sao Khue Luu, Khanh-Tung Tran +5

Effective tool use is essential for agentic AI, yet training agents to utilize tools remains challenging due to manually designed rewards, limited training data, and poor multi-too…

cs.CL2026

Qomhra: A Bilingual Irish and English Large Language Model

Joseph McInerney, Khanh-Tung Tran, Liam Lonergan +3

Large language model (LLM) research and development has overwhelmingly focused on the world's major languages, leading to under-representation of low-resource languages such as Iri…

cs.CL2025

Reasoning Transfer for an Extremely Low-Resource and Endangered Language: Bridging Languages Through Sample-Efficient Language Understanding

Khanh-Tung Tran, Barry O'Sullivan, Hoang D. Nguyen

Recent advances have enabled Large Language Models (LLMs) to tackle reasoning tasks by generating chain-of-thought (CoT) rationales, yet these gains have largely applied to high-re…

cs.CL2025

Irish-BLiMP: A Linguistic Benchmark for Evaluating Human and Language Model Performance in a Low-Resource Setting

Josh McGiff, Khanh-Tung Tran, William Mulcahy +7

We present Irish-BLiMP (Irish Benchmark of Linguistic Minimal Pairs), the first dataset and framework designed for fine-grained evaluation of linguistic competence in the Irish lan…

cs.CL2025

IRLBench: A Multi-modal, Culturally Grounded, Parallel Irish-English Benchmark for Open-Ended LLM Reasoning Evaluation

Khanh-Tung Tran, Barry O'Sullivan, Hoang D. Nguyen

Recent advances in Large Language Models (LLMs) have demonstrated promising knowledge and reasoning abilities, yet their performance in multilingual and low-resource settings remai…