Large Language
Large language models (LLMs) are rapidly advancing artificial intelligence, aiming to create systems capable of understanding and generating human-like text. Current research focuses on improving efficiency (e.g., through speculative decoding), exploring their intriguing properties in multimodal contexts (combining language with vision), and applying them to diverse fields like healthcare, manufacturing, and software engineering. This work is significant because LLMs are already impacting various sectors, offering potential for improved decision-making, automation, and personalized experiences, while also raising important questions about robustness, security, and ethical implications.
Papers
Retrieving Implicit and Explicit Emotional Events Using Large Language Models
Guimin Hu, Hasti Seifi
LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples
Huiyu Wu, Diego Klabjan
BATON: Enhancing Batch-wise Inference Efficiency for Large Language Models via Dynamic Re-batching
Peizhuang Cong, Qizhi Chen, Haochen Zhao, Tong Yang
ChineseSafe: A Chinese Benchmark for Evaluating Safety in Large Language Models
Hengxiang Zhang, Hongfu Gao, Qiang Hu, Guanhua Chen, Lili Yang, Bingyi Jing, Hongxin Wei, Bing Wang, Haifeng Bai, Lei Yang
AdaEDL: Early Draft Stopping for Speculative Decoding of Large Language Models via an Entropy-based Lower Bound on Token Acceptance Probability
Sudhanshu Agrawal, Wonseok Jeon, Mingu Lee
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning
Jingfan Zhang, Yi Zhao, Dan Chen, Xing Tian, Huanran Zheng, Wei Zhu
GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration
Xin Li, Qizhi Chu, Yubin Chen, Yang Liu, Yaoqi Liu, Zekai Yu, Weize Chen, Chen Qian, Chuan Shi, Cheng Yang
From Attention to Activation: Unravelling the Enigmas of Large Language Models
Prannay Kaul, Chengcheng Ma, Ismail Elezi, Jiankang Deng
Large Language Model-based Augmentation for Imbalanced Node Classification on Text-Attributed Graphs
Leyao Wang, Yu Wang, Bo Ni, Yuying Zhao, Tyler Derr
Semantic-guided Search for Efficient Program Repair with Large Language Models
Thanh Le-Cong, Bach Le, Toby Murray
MIRA: A Method of Federated MultI-Task Learning for LaRge LAnguage Models
Ahmed Elbakary, Chaouki Ben Issaid, Tamer ElBatt, Karim Seddik, Mehdi Bennis
Hallucination Detox: Sensitive Neuron Dropout (SeND) for Large Language Model Training
Shahrad Mohammadzadeh, Juan David Guerra, Marco Bonizzato, Reihaneh Rabbany, Golnoosh Farnadi
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning
Shengjie Sun, Runze Liu, Jiafei Lyu, Jing-Wen Yang, Liangpeng Zhang, Xiu Li
Automated Genre-Aware Article Scoring and Feedback Using Large Language Models
Chihang Wang, Yuxin Dong, Zhenhong Zhang, Ruotong Wang, Shuo Wang, Jiajing Chen
UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models
Yuzhe Yang, Yifei Zhang, Yan Hu, Yilin Guo, Ruoli Gan, Yueru He, Mingcong Lei, Xiao Zhang, Haining Wang, Qianqian Xie, Jimin Huang, Honghai Yu, Benyou Wang
Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models
Eddie L. Ungless, Nikolas Vitsakis, Zeerak Talat, James Garforth, Björn Ross, Arno Onken, Atoosa Kasirzadeh, Alexandra Birch