collaborators

6 papers

cs.AI2026

Agentic Design Patterns: A System-Theoretic Framework

Minh-Dung Dao, Quy Minh Le, Hoang Thanh Lam +4

With the development of foundation model (FM), agentic AI systems are getting more attention, yet their inherent issues like hallucination and poor reasoning, coupled with the freq…

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.AI2025

Questionnaire meets LLM: A Benchmark and Empirical Study of Structural Skills for Understanding Questions and Responses

Duc-Hai Nguyen, Vijayakumar Nanjappan, Barry O'Sullivan +1

Millions of people take surveys every day, from market polls and academic studies to medical questionnaires and customer feedback forms. These datasets capture valuable insights, b…

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…

cs.AI2025

Multi-Agent Collaboration Mechanisms: A Survey of LLMs

Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen +3

With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reaso…