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
Insights into the Lottery Ticket Hypothesis and Iterative Magnitude Pruning
Tausifa Jan Saleem, Ramanjit Ahuja, Surendra Prasad, Brejesh Lall
Comprehensive Evaluation and Insights into the Use of Large Language Models in the Automation of Behavior-Driven Development Acceptance Test Formulation
Shanthi Karpurapu, Sravanthy Myneni, Unnati Nettur, Likhit Sagar Gajja, Dave Burke, Tom Stiehm, Jeffery Payne
From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models
Kung-Hsiang Huang, Hou Pong Chan, Yi R. Fung, Haoyi Qiu, Mingyang Zhou, Shafiq Joty, Shih-Fu Chang, Heng Ji
Word Order's Impacts: Insights from Reordering and Generation Analysis
Qinghua Zhao, Jiaang Li, Lei Li, Zenghui Zhou, Junfeng Liu
Can Audio Reveal Music Performance Difficulty? Insights from the Piano Syllabus Dataset
Pedro Ramoneda, Minhee Lee, Dasaem Jeong, J. J. Valero-Mas, Xavier Serra
Detecting AI-Generated Sentences in Human-AI Collaborative Hybrid Texts: Challenges, Strategies, and Insights
Zijie Zeng, Shiqi Liu, Lele Sha, Zhuang Li, Kaixun Yang, Sannyuya Liu, Dragan Gašević, Guanliang Chen
Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy, Survey and Insights
Yuan Wang, Lokesh Kumar Sambasivan, Mingang Fu, Prakhar Mehrotra
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model
Hao Cheng, Erjia Xiao, Jindong Gu, Le Yang, Jinhao Duan, Jize Zhang, Jiahang Cao, Kaidi Xu, Renjing Xu
Investigating Continual Pretraining in Large Language Models: Insights and Implications
Çağatay Yıldız, Nishaanth Kanna Ravichandran, Prishruit Punia, Matthias Bethge, Beyza Ermis
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