Large Language Model
Large language models (LLMs) are sophisticated AI systems designed to process and generate human-like text, aiming to improve various natural language processing tasks. Current research focuses on enhancing LLM safety, efficiency (through techniques like quantization and optimized decoding), and fairness, as well as improving their ability to perform complex reasoning and handle diverse instructions. These advancements are significant because they address critical limitations in current LLMs and pave the way for broader applications across diverse fields, including healthcare, legal tech, and autonomous systems.
Papers
Decoding specialised feature neurons in LLMs with the final projection layer
Harry J Davies
Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense
Yang Ouyang, Hengrui Gu, Shuhang Lin, Wenyue Hua, Jie Peng, Bhavya Kailkhura, Tianlong Chen, Kaixiong Zhou
LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignment
Yifei Liu, Hengwei Ye, Shuhang Li
Transformers Simulate MLE for Sequence Generation in Bayesian Networks
Yuan Cao, Yihan He, Dennis Wu, Hong-Yu Chen, Jianqing Fan, Han Liu
Decoding News Bias: Multi Bias Detection in News Articles
Bhushan Santosh Shah, Deven Santosh Shah, Vahida Attar
Hengqin-RA-v1: Advanced Large Language Model for Diagnosis and Treatment of Rheumatoid Arthritis with Dataset based Traditional Chinese Medicine
Yishen Liu, Shengda Luo, Zishao Zhong, Tongtong Wu, Jianguo Zhang, Peiyao Ou, Yong Liang, Liang Liu, Hudan Pan
Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap
Hyunwoo Ko, Guijin Son, Dasol Choi
Efficient Deployment of Large Language Models on Resource-constrained Devices
Zhiwei Yao, Yang Xu, Hongli Xu, Yunming Liao, Zuan Xie
A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization
Amitava Das, Suranjana Trivedy, Danush Khanna, Rajarshi Roy, Gurpreet Singh, Basab Ghosh, Yaswanth Narsupalli, Vinija Jain, Vasu Sharma, Aishwarya Naresh Reganti, Aman Chadha
UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility
Yonglin Tian, Fei Lin, Yiduo Li, Tengchao Zhang, Qiyao Zhang, Xuan Fu, Jun Huang, Xingyuan Dai, Yutong Wang, Chunwei Tian, Bai Li, Yisheng Lv, Levente Kovács, Fei-Yue Wang
AdaSkip: Adaptive Sublayer Skipping for Accelerating Long-Context LLM Inference
Zhuomin He, Yizhen Yao, Pengfei Zuo, Bin Gao, Qinya Li, Zhenzhe Zheng, Fan Wu
Financial Named Entity Recognition: How Far Can LLM Go?
Yi-Te Lu, Yintong Huo
On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passing
Jianwei Wang, Kai Wang, Ying Zhang, Wenjie Zhang, Xiwei Xu, Xuemin Lin
Personalized Graph-Based Retrieval for Large Language Models
Steven Au, Cameron J. Dimacali, Ojasmitha Pedirappagari, Namyong Park, Franck Dernoncourt, Yu Wang, Nikos Kanakaris, Hanieh Deilamsalehy, Ryan A. Rossi, Nesreen K. Ahmed
Instruction-Following Pruning for Large Language Models
Bairu Hou, Qibin Chen, Jianyu Wang, Guoli Yin, Chong Wang, Nan Du, Ruoming Pang, Shiyu Chang, Tao Lei
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Weizhi Zhang, Yuanchen Bei, Liangwei Yang, Henry Peng Zou, Peilin Zhou, Aiwei Liu, Yinghui Li, Hao Chen, Jianling Wang, Yu Wang, Feiran Huang, Sheng Zhou, Jiajun Bu, Allen Lin, James Caverlee, Fakhri Karray, Irwin King, Philip S. Yu
Auto-RT: Automatic Jailbreak Strategy Exploration for Red-Teaming Large Language Models
Yanjiang Liu, Shuhen Zhou, Yaojie Lu, Huijia Zhu, Weiqiang Wang, Hongyu Lin, Ben He, Xianpei Han, Le Sun
SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation
Mingjie Li, Wai Man Si, Michael Backes, Yang Zhang, Yisen Wang
Automating Legal Concept Interpretation with LLMs: Retrieval, Generation, and Evaluation
Kangcheng Luo, Quzhe Huang, Cong Jiang, Yansong Feng
LLMs & Legal Aid: Understanding Legal Needs Exhibited Through User Queries
Michal Kuk, Jakub Harasta