58 citations · 65 across the 18 of their papers we have counts for
7 papers · 1 filter
Disentangling Topology and Diversity in Multi-Agent LLMs for Multilingual Low-Resource Emotion Detection
Ulugbek Shernazarov, Charitha Ruwansiri Weerakon Basnayake, Abdelkhaleq El Jarjini +2
Multi-agent LLM systems combine multiple inference calls, but prior work often confounds how calls are connected with how they are diversified. We study these factors independently…
Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detection
Razieh Chalehchaleh, Reza Farahbakhsh, Noel Crespi
Large Language Models (LLMs) are increasingly used for automated fact-checking, yet their susceptibility to gender bias in this context remains underexplored. This study presents t…
RetrievalRouter: Joint Modality and Architecture Selection for Document Retrieval
Emre Kuru, Mehmet Onur Keskin, Reza Farahbakhsh +1
Document retrieval increasingly supports high-stakes information access in finance, healthcare, and law. Modern retrieval pipelines vary both in modality (text or multimodal) and i…
PTEI: Integrating Personality Traits to Enhance Emotional Intelligence in Large Language Models
Amir Reza Jafari, Praboda Rajapaksha, Reza Farahbakhsh +1
Despite advances in Emotional Intelligence (EI), Large Language Models (LLMs) still significantly underperform humans in complex emotional reasoning. This gap originates partly fro…
Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework
Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li +8
Automated medical report generation for 3D PET/CT imaging is fundamentally challenged by the high-dimensional nature of volumetric data and a critical scarcity of annotated dataset…
Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models
Minh Khoi Nguyen, Dai Lam Le, Amir Reza Jafari +8
Large vision-language models (VLMs) demonstrate strong performance in medical image understanding, but frequently generate clinically plausible yet incorrect statements, raising si…