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
Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation
Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, Suhail Doshi
Does Negative Sampling Matter? A Review with Insights into its Theory and Applications
Zhen Yang, Ming Ding, Tinglin Huang, Yukuo Cen, Junshuai Song, Bin Xu, Yuxiao Dong, Jie Tang
Evaluating Roadside Perception for Autonomous Vehicles: Insights from Field Testing
Rusheng Zhang, Depu Meng, Shengyin Shen, Tinghan Wang, Tai Karir, Michael Maile, Henry X. Liu
Revolutionizing Finance with LLMs: An Overview of Applications and Insights
Huaqin Zhao, Zhengliang Liu, Zihao Wu, Yiwei Li, Tianze Yang, Peng Shu, Shaochen Xu, Haixing Dai, Lin Zhao, Gengchen Mai, Ninghao Liu, Tianming Liu
Evaluating the Impact of Personalized Value Alignment in Human-Robot Interaction: Insights into Trust and Team Performance Outcomes
Shreyas Bhat, Joseph B. Lyons, Cong Shi, X. Jessie Yang
Adversarial Doodles: Interpretable and Human-drawable Attacks Provide Describable Insights
Ryoya Nara, Yusuke Matsui