System Description
System descriptions encompass the design, implementation, and evaluation of computational systems addressing diverse challenges. Current research focuses on improving system efficiency and accuracy through techniques like hybrid neural networks for optimal control, fine-tuned BERT models for question answering, and various large language model (LLM) applications for tasks ranging from automatic scoring to creative idea generation. These advancements are significant for improving automation in various fields, from energy management and disaster response to healthcare and education, and for advancing our understanding of AI capabilities and limitations.
Papers
Sentiment and Emotion-aware Multi-criteria Fuzzy Group Decision Making System
Adilet Yerkin, Pakizar Shamoi, Elnara Kadyrgali
Xinyu: An Efficient LLM-based System for Commentary Generation
Yiquan Wu, Bo Tang, Chenyang Xi, Yu Yu, Pengyu Wang, Yifei Liu, Kun Kuang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Jie Hu, Peng Cheng, Zhonghao Wang, Yi Wang, Yi Luo, Mingchuan Yang
A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suites
Andrea Lops, Fedelucio Narducci, Azzurra Ragone, Michelantonio Trizio, Claudio Bartolini
On learning capacities of Sugeno integrals with systems of fuzzy relational equations
Ismaïl Baaj
LiveFC: A System for Live Fact-Checking of Audio Streams
Venktesh V, Vinay Setty