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
ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources
Shuting Yang, Zehui Liu, Wolfgang Mayer
'Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMs
Ishika Joshi, Ishita Gupta, Adrita Dey, Tapan Parikh
AQA: Adaptive Question Answering in a Society of LLMs via Contextual Multi-Armed Bandit
Mohanna Hoveyda, Arjen P. de Vries, Harrie Oosterhuis, Maarten de Rijke, Faegheh Hasibi
Hey Robot! Personalizing Robot Navigation through Model Predictive Control with a Large Language Model
Diego Martinez-Baselga, Oscar de Groot, Luzia Knoedler, Javier Alonso-Mora, Luis Riazuelo, Luis Montano
LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench
Karthik Valmeekam, Kaya Stechly, Subbarao Kambhampati
Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time
David Herel, Vojtech Bartek, Tomas Mikolov
SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation
Jinge Wu, Yunsoo Kim, Daqian Shi, David Cliffton, Fenglin Liu, Honghan Wu
Large Language Model Should Understand Pinyin for Chinese ASR Error Correction
Yuang Li, Xiaosong Qiao, Xiaofeng Zhao, Huan Zhao, Wei Tang, Min Zhang, Hao Yang
Leveraging Knowledge Graphs and LLMs to Support and Monitor Legislative Systems
Andrea Colombo
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information
Yuxin Wang, Minghua Ma, Zekun Wang, Jingchang Chen, Huiming Fan, Liping Shan, Qing Yang, Dongliang Xu, Ming Liu, Bing Qin
ControlMath: Controllable Data Generation Promotes Math Generalist Models
Nuo Chen, Ning Wu, Jianhui Chang, Jia Li
An adapted large language model facilitates multiple medical tasks in diabetes care
Lai Wei, Zhen Ying, Muyang He, Yutong Chen, Qian Yang, Yanzhe Hong, Jiaping Lu, Xiaoying Li, Weiran Huang, Ying Chen
RRM: Robust Reward Model Training Mitigates Reward Hacking
Tianqi Liu, Wei Xiong, Jie Ren, Lichang Chen, Junru Wu, Rishabh Joshi, Yang Gao, Jiaming Shen, Zhen Qin, Tianhe Yu, Daniel Sohn, Anastasiia Makarova, Jeremiah Liu, Yuan Liu, Bilal Piot, Abe Ittycheriah, Aviral Kumar, Mohammad Saleh
What Would You Ask When You First Saw $a^2+b^2=c^2$? Evaluating LLM on Curiosity-Driven Questioning
Shashidhar Reddy Javaji, Zining Zhu
Guided Profile Generation Improves Personalization with LLMs
Jiarui Zhang
LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models
Akshaj Kumar Veldanda, Shi-Xiong Zhang, Anirban Das, Supriyo Chakraborty, Stephen Rawls, Sambit Sahu, Milind Naphade
Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner
Yuzhang Shang, Bingxin Xu, Weitai Kang, Mu Cai, Yuheng Li, Zehao Wen, Zhen Dong, Kurt Keutzer, Yong Jae Lee, Yan Yan
Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization
Mohammad Samragh, Iman Mirzadeh, Keivan Alizadeh Vahid, Fartash Faghri, Minsik Cho, Moin Nabi, Devang Naik, Mehrdad Farajtabar
Knowledge-Based Domain-Oriented Data Augmentation for Enhancing Unsupervised Sentence Embedding
Peichao Lai, Zhengfeng Zhang, Bin Cui
A New Perspective on ADHD Research: Knowledge Graph Construction with LLMs and Network Based Insights
Hakan T. Otal, Stephen V. Faraone, M. Abdullah Canbaz