2 citations · 3 across the 8 of their papers we have counts for
8 papers
UTS at CheckThat! 2026: Cite-Frame Engineering for Generated Fact-Checking Articles
Dima Galat, Marian-Andrei Rizoiu
CheckThat! 2026 Task 3 asks systems to generate fact-checking articles, graded by an unweighted mean of four sub-metrics (M4). Our UTS submission placed 2nd of 11 teams (M4 = 0.484…
UTS at ELOQUENT 2026 Voight-Kampff: structural shifts in AI writing bypass state-of-the-art detectors
Dima Galat, Marian-Andrei Rizoiu
We investigate which language model evasion attacks survive state-of-the-art adversarial fine-tuning, developing strategies that sweep the top 5 positions on the ELOQUENT 2026 Voig…
Cost-Pragmatic Quality Gating and Selection-Fusion Multi-Model Combiners for BioASQ Phases A+ and B
Dima Galat, Marian-Andrei Rizoiu
We describe our BioASQ Task 14B 2026 system. The work centers on two design decisions: how aggressively to re-retrieve when first-stage retrieval is weak, and how to combine multip…
UTS at PsyDefDetect: Multi-Agent Councils and Absence-Based Reasoning for Defense Mechanism Classification
Dima Galat, Marian-Andrei Rizoiu
This paper describes our system for classifying psychological defense mechanisms in emotional support dialogues using the Defense Mechanism Rating Scales (DMRS), placing second (F1…
LLM Ensemble for RAG: Role of Context Length in Zero-Shot Question Answering for BioASQ Challenge
Dima Galat, Diego Molla-Aliod
Biomedical question answering (QA) poses significant challenges due to the need for precise interpretation of specialized knowledge drawn from a vast, complex, and rapidly evolving…
Advancing LLM detection in the ALTA 2024 Shared Task: Techniques and Analysis
Dima Galat
The recent proliferation of AI-generated content has prompted significant interest in developing reliable detection methods. This study explores techniques for identifying AI-gener…
Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training
Dima Galat, Marian-Andrei Rizoiu
Biomedical summarization requires large datasets to train for text generation. We show that while transfer learning offers a viable option for addressing this challenge, an in-doma…
Query-Focused Extractive Summarisation for Finding Ideal Answers to Biomedical and COVID-19 Questions
Diego Mollá, Urvashi Khanna, Dima Galat +2
This paper presents Macquarie University's participation to the BioASQ Synergy Task, and BioASQ9b Phase B. In each of these tasks, our participation focused on the use of query-foc…