Results 101 to 110 of about 26,588 (249)

Universal Battery Capacity Degradation Forecasting Driven by Foundation Models Across Diverse Chemistries and Conditions

Advanced Science, EarlyView.
A unified time‐series forecasting framework learns transferable battery capacity‐degradation patterns from 20 heterogeneous datasets spanning chemistries, formats, temperatures, and cycling conditions. A single model delivers competitive predictions on both known and previously unseen datasets, while physics‐guided representation learning improves ...
Joey Chan, Huan Wang, Zhen Chen, Haoyu Pan, Wei Wu, Zirong Wang, Ershun Pan, Min Xie, Lifeng Xi   +8 more
wiley   +1 more source

Legal large language models (LLMs): legal dynamos or “fancifully packaged ChatGPT”?

Discover Artificial Intelligence
This brief comment considers the impact of large language models (LLMs) specifically designed for legal tasks. In the author’s view, conversations regarding the use of such LLMs in the legal profession have been considered from various extremes depending
Fife Ogunde
doaj   +1 more source

JorGPT: Instructor-Aided Grading of Programming Assignments with Large Language Models (LLMs)

Future Internet
This paper explores the application of large language models (LLMs) to automate the evaluation of programming assignments in an undergraduate “Introduction to Programming” course.
Jorge Cisneros-González, Natalia Gordo-Herrera, Iván Barcia-Santos, Javier Sánchez-Soriano   +3 more
doaj   +1 more source

Political-LLM: Large Language Models in Political Science

CoRR
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection. Meanwhile, the need to systematically understand how LLMs can further revolutionize the field also becomes urgent.
Lincan Li, Jiaqi Li, Catherine Chen 0003, Fred Gui, Hongjia Yang, Chenxiao Yu, Zhengguang Wang, Jianing Cai, Junlong Aaron Zhou, Bolin Shen, Alex Qian, Weixin Chen, Zhongkai Xue, Lichao Sun 0001, Lifang He 0001, Hanjie Chen, Kaize Ding, Zijian Du, Fangzhou Mu, Jiaxin Pei, Jieyu Zhao 0001, Swabha Swayamdipta, Willie Neiswanger, Hua Wei 0001, Xiyang Hu, Shixiang Zhu, Tianlong Chen 0001, Yingzhou Lu, Yang Shi, Lianhui Qin, Tianfan Fu, Zhengzhong Tu, Yuzhe Yang 0005, Jaemin Yoo, Jiaheng Zhang, Ryan A. Rossi, Liang Zhan, Liang Zhao 0002, Emilio Ferrara, Yan Liu 0002, Furong Huang, Xiangliang Zhang 0001, Lawrence Rothenberg, Shuiwang Ji, Philip S. Yu, Yue Zhao 0016, Yushun Dong   +46 more
openaire   +3 more sources

Electroforming‐Free, Self‐Rectifying Selector‐Only Memory With Diffusive Cu‐Ion Dynamics for Logic‐In‐Memory Computing

Advanced Science, EarlyView.
Electroforming‐free, self‐rectifying switching with polarity‐dependent threshold modulation is realized in selector‐only memory through Cu‐ion migration in a bilayer stacked dual functional materials. Ultrafast rupturing of Cu filament enables drift‐free operation with high stability.
Jae‐Kyeong Kim, Dae‐Seong Woo, Min‐Jong Han, Hong‐Gwang Seo, Gwang‐Ho Park, In‐Taek Woo, Jea‐Gun Park, Jinsub Park   +7 more
wiley   +1 more source

SKR1: Benchmark for Testing Knowledge About Slovak Realia for Large Language Models

Acta Informatica Pragensia
Background: To objectively evaluate the capabilities of large language models (LLMs), we need to develop tools that enable such assessment. While numerous benchmarks exist, the vast majority are in English and focus on general knowledge, often ...
Marek Dobeš
doaj   +1 more source

Structural Divergence Between the Moltbook AI‐Agent Network and Human Social Networks

Advanced Science, EarlyView.
Analysis of the Moltbook AI‐agent network reveals a striking combination of familiar global scaling and distinct internal organization. Attention is highly concentrated, reciprocity is limited, connected triads are suppressed, and communities are strongly modular.
Wenpin Hou, Zhicheng Ji
wiley   +1 more source

Towards a Compositional Framework for Describing Human Phenotypes

Advanced Science, EarlyView.
The Phenotype Assembly Method (PhenoAM) decomposes phenotype variables into measurable Features and typed Qualifiers, enabling standardized, machine‐readable Phenome Data Elements (PhenoDEs) that preserve measurement context. Applied in the International Human Phenome Project (IHPP), the framework yields 58 371 PhenoDEs and supports component‐level ...
Wanting Hu, Yunchao Ling, Xinyue Hu, Wei Chen, Rong Zhou, Xinhao Zhuang, Yanfeng Jiang, Jialu Zhao, Xingdong Chen, Jingchun Luo, Li Jin, Guoqing Zhang   +11 more
wiley   +1 more source

Generative AI and the rise of deepfake technology: a journey from traditional techniques to LLM integration

Discover Applied Sciences
Generative artificial intelligence (AI) is advancing rapidly, with deepfake technology illustrating the intersection of AI and multimedia forensics. This study examines the classification and evolution of deepfake, exploring their definition, societal ...
Fariha Jahan, Sirajum Munira Shifat, Md. Kishor Morol, Nafiz Fahad, Kah Ong Michael Goh, Dip Nandi, Md. Abdullah-Al-Jubair, Md. Jakir Hossen   +7 more
doaj   +1 more source

Survey of Different Large Language Model Architectures: Trends, Benchmarks, and Challenges

IEEE Access
Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries.
Minghao Shao, Abdul Basit, Ramesh Karri, Muhammad Shafique   +3 more
doaj   +1 more source