23 citations · 23 across the 2 of their papers we have counts for
6 papers
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…
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…
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…
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…
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…
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…