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
System Test Case Design from Requirements Specifications: Insights and Challenges of Using ChatGPT
Shreya Bhatia, Tarushi Gandhi, Dhruv Kumar, Pankaj Jalote
Predicting Pedestrian Crossing Behavior in Germany and Japan: Insights into Model Transferability
Chi Zhang (1), Janis Sprenger (2), Zhongjun Ni (3), Christian Berger (1) ((1) Department of Computer Science and Engineering, University of Gothenburg, Sweden, (2) German Research Center for Artificial Intelligence (DFKI), Saarland Informatics Campus, Germany, (3) Department of Science and Technology, Linköping University, Campus Norrköping, Sweden)
Exploring trends in audio mixes and masters: Insights from a dataset analysis
Angeliki Mourgela, Elio Quinton, Spyridon Bissas, Joshua D. Reiss, David Ronan