Large Scale
Large-scale data processing and analysis are central to addressing numerous scientific and engineering challenges, focusing on efficient handling of massive datasets and complex systems. Current research emphasizes developing novel algorithms and model architectures, such as graph neural networks, deep learning models, and physics-guided machine learning, to improve efficiency, accuracy, and scalability in diverse applications. These advancements are crucial for tackling problems ranging from traffic optimization and robot navigation to astronomical surveys and the development of more energy-efficient AI systems. The resulting insights and tools have significant implications across various fields, enabling more effective data-driven decision-making and scientific discovery.
Papers
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data
Bo Peng, Xinyi Ling, Ziru Chen, Huan Sun, Xia Ning
Multi-Level GNN Preconditioner for Solving Large Scale Problems
Matthieu Nastorg, Jean-Marc Gratien, Thibault Faney, Michele Alessandro Bucci, Guillaume Charpiat, Marc Schoenauer
Scalable Qualitative Coding with LLMs: Chain-of-Thought Reasoning Matches Human Performance in Some Hermeneutic Tasks
Zackary Okun Dunivin
Query of CC: Unearthing Large Scale Domain-Specific Knowledge from Public Corpora
Zhaoye Fei, Yunfan Shao, Linyang Li, Zhiyuan Zeng, Conghui He, Hang Yan, Dahua Lin, Xipeng Qiu
Large Language Model Lateral Spear Phishing: A Comparative Study in Large-Scale Organizational Settings
Mazal Bethany, Athanasios Galiopoulos, Emet Bethany, Mohammad Bahrami Karkevandi, Nishant Vishwamitra, Peyman Najafirad
EfficientRec an unlimited user-item scale recommendation system based on clustering and users interaction embedding profile
Vu Hong Quan, Le Hoang Ngan, Le Minh Duc, Nguyen Tran Ngoc Linh, Hoang Quynh-Le