Results 221 to 230 of about 239,466 (252)

A Comprehensive Overview of Large Language Models

ACM Transactions on Intelligent Systems and Technology, 2023
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction.
Humza Naveed, Asad Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Nick Barnes, Ajmal Saeed Mian   +7 more
semanticscholar   +1 more source

Large Language Models (LLMs) Inference Offloading and Resource Allocation in Cloud-Edge Computing: An Active Inference Approach

IEEE Transactions on Mobile Computing
With the increasing popularity and demands for large language model applications on mobile devices, it is difficult for resource-limited mobile terminals to run large-model inference tasks efficiently.
Ying He, Jingcheng Fang, F. Yu, V. Leung
semanticscholar   +1 more source

A survey on multimodal large language models

National Science Review, 2023
Recently, the multimodal large language model (MLLM) represented by GPT-4V has been a new rising research hotspot, which uses powerful large language models (LLMs) as a brain to perform multimodal tasks.
Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, Enhong Chen   +6 more
semanticscholar   +1 more source

Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey

Trans. Mach. Learn. Res.
Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding.
X. Fang, Weijie Xu, F. Tan, Jiani Zhang, Ziqing Hu, Yanjun Qi, Scott Nickleach, Diego Socolinsky, Srinivasan H. Sengamedu, Christos Faloutsos   +9 more
semanticscholar   +1 more source

A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directions

arXiv.org
The recent progression of Large Language Models (LLMs) has witnessed great success in the fields of data-centric applications. LLMs trained on massive textual datasets showed ability to encode not only context but also ability to provide powerful ...
M. Hassanin, Nour Moustafa
semanticscholar   +1 more source

Large Language Models (LLMs) for Materials Design

Advanced Functional Materials
In recent years, rapid advances in large language models (LLMs) have been witnessed, while materials scientists have quickly adapted to exploit their potential. This review surveys the latest developments at the intersection of LLMs and materials science.
Lei Zhang, Zhaorui Liu, Ben Ni, Quan Wang   +3 more
semanticscholar   +1 more source

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

arXiv.org
Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}.
Shuming Ma, Hongyu Wang, Lingxiao Ma, Lei Wang, Wenhui Wang, Shaohan Huang, Lifeng Dong, Ruiping Wang, Jilong Xue, Furu Wei   +9 more
semanticscholar   +1 more source

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

International Conference on Learning Representations
Large Language Models (LLMs) applied to code-related applications have emerged as a prominent field, attracting significant interest from both academia and industry.
Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, Ion Stoica   +9 more
semanticscholar   +1 more source

A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models

Knowledge Discovery and Data Mining
As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks.
Wenqi Fan, Yujuan Ding, Liang-bo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, Qing Li   +7 more
semanticscholar   +1 more source

Toward expert-level medical question answering with large language models

Nature Medicine
Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a ‘passing’ score in United States Medical Licensing Examination style questions.
Karan Singhal, Tao Tu, Juraj Gottweis, R. Sayres, Ellery Wulczyn, Mohamed Amin, Le Hou, Kevin Clark, Stephen R. Pfohl, Heather Cole-Lewis, Darlene Neal, Q. Rashid, Mike Schaekermann, Amy Wang, D. Dash, Jonathan H. Chen, Nigam H. Shah, Sami Lachgar, P. Mansfield, Sushant Prakash, Bradley Green, Ewa Dominowska, Blaise Agüera y Arcas, Nenad Tomašev, Yun Liu, Renee Wong, Christopher Semturs, S. Mahdavi, Joelle K. Barral, Dale R. Webster, G. Corrado, Yossi Matias, Shekoofeh Azizi, A. Karthikesalingam, Vivek Natarajan   +34 more
semanticscholar   +1 more source