Results 11 to 20 of about 515,234 (309)

Large language models for bioinformatics

Quantitative Biology
With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and ...
Wei Ruan, Yanjun Lyu, Jing Zhang, Jiazhang Cai, Peng Shu, Yang Ge, Yao Lu, Shang Gao, Yue Wang, Peilong Wang, Lin Zhao, Tao Wang, Yufang Liu, Luyang Fang, Ziyu Liu, Zhengliang Liu, Yiwei Li, Zihao Wu, Junhao Chen, Hanqi Jiang, Yi Pan, Zhenyuan Yang, Jingyuan Chen, Shizhe Liang, Wei Zhang, Terry Ma, Yuan Dou, Jianli Zhang, Xinyu Gong, Qi Gan, Yusong Zou, Zebang Chen, Yuanxin Qian, Shuo Yu, Jin Lu, Kenan Song, Xianqiao Wang, Andrea Sikora, Gang Li, Xiang Li, Quanzheng Li, Yingfeng Wang, Lu Zhang, Yohannes Abate, Lifang He, Wenxuan Zhong, Rongjie Liu, Chao Huang, Wei Liu, Ye Shen, Ping Ma, Hongtu Zhu, Yajun Yan, Dajiang Zhu, Tianming Liu   +54 more
doaj   +4 more sources

Large Language Models

NEJM Evidence, 2023
Large Language ModelsIn the latest edition of Stats, STAT!, Fralick and colleagues explain the statistics behind large language models - used in chat bots like ChatGPT and Bard. While these new tools may seem remarkably intelligent, at their core they just assemble sentences based on statistics from large amounts of text.
Michael, Fralick, Chana A, Sacks, Daniel, Muller, Tim, Vining, Emily, Ling, Jeffrey M, Drazen, C Corey, Hardin   +6 more
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Large Language Models in Medicine [PDF]

Healthcare Informatics Research
Jinwook Choi
doaj   +3 more sources

OffensEval 2023: Offensive language identification in the age of Large Language Models [PDF]

, 2023
The OffensEval shared tasks organized as part of SemEval-2019–2020 were very popular, attracting over 1300 participating teams. The two editions of the shared task helped advance the state of the art in offensive language identification by providing the ...
Ranasinghe, Tharindu, Nakov, Preslav, Zampieri, Marcos, Dmonte, Alphaeus, Rosenthal, Sara   +4 more
core   +1 more source

Autoformalization with Large Language Models

Advances in Neural Information Processing Systems 35, 2022
Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could advance the fields of formal verification, program synthesis, and artificial intelligence.
Wu, Yuhuai, Jiang, Qiaochu, Li, Wenda, Rabe, Markus, Staats, Charles, Jamnik, Mateja, Szegedy, Christian   +6 more
openaire   +4 more sources

Leveraging Computational Psychometrics for Language Testing [PDF]

, 2023
The recent surge in the popularity of Large Language Models (LLM) for language assessment underscores the growing significance of cost-effective language evaluation in our increasingly digitalized society.
Ardeshir Geranpayeh
core   +1 more source

Medical education empowered by generative artificial intelligence large language models

, 2023
Generative artificial intelligence (GAI) large language models (LLMs), like ChatGPT, have become the world's fastest growing applications. Here, we provide useful strategies for educators in medical and health science (M&HS) to integrate GAI-LLMs ...
Todorovic, Michael, Singleton, R, Jessica Stokes-Parish, Stokes-Parish, Jessica, Todorovic, M, Stokes-Parish, J, Michael Todorovic, Jowsey, Tanisha, Singleton, Rachelle, Tanisha Jowsey, Rachelle Singleton, Jowsey, T   +11 more
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Large Language Models as Optimizers

CoRR, 2023
ICLR 2024; 42 pages, 26 figures, 15 tables.
Chengrun Yang, Xuezhi Wang 0002, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen   +6 more
openaire   +3 more sources

A Watermark for Large Language Models

CoRR, 2023
Potential harms of large language models can be mitigated by watermarking model output, i.e., embedding signals into generated text that are invisible to humans but algorithmically detectable from a short span of tokens. We propose a watermarking framework for proprietary language models.
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein   +5 more
openaire   +3 more sources

Integrating Graphs with Large Language Models: Methods and Prospects

, 2023
Large language models (LLMs) such as GPT-4 have emerged as frontrunners, showcasing unparalleled prowess in diverse applications, including answering queries, code generation, and more.
Zheng, Yizhen, Pan, Shirui, Liu, Yixin
core   +1 more source