DCU Insight AQ
DCU Insight AQ is not a defined scientific topic or project readily identifiable in the provided abstracts. The abstracts cover a broad range of research using Large Language Models (LLMs) and other machine learning techniques across diverse fields, including legal reasoning, medical diagnosis, materials science, and anomaly detection. Current research focuses on improving LLM performance through techniques like multi-agent frameworks, multimodal data integration, and careful data curation, as well as addressing challenges such as hallucinations, bias, and efficient model training. These advancements have the potential to significantly improve data analysis, automate complex tasks, and enhance decision-making across numerous scientific and industrial domains.
Papers
TrialGraph: Machine Intelligence Enabled Insight from Graph Modelling of Clinical Trials
Christopher Yacoumatos, Stefano Bragaglia, Anshul Kanakia, Nils Svangård, Jonathan Mangion, Claire Donoghue, Jim Weatherall, Faisal M. Khan, Khader Shameer
Chimpanzee voice prints? Insights from transfer learning experiments from human voices
Mael Leroux, Orestes Gutierrez Al-Khudhairy, Nicolas Perony, Simon W. Townsend