Results 121 to 130 of about 22,074,380 (291)

Large Language Model (LLM) Generated Personalized Mnemonics [PDF]

Mnemonic devices aid information retention and retrieval of information from long-term memory. The peculiarity of mnemonic devices makes them memorable. An example of a mnemonic device is My Very Educated Mother Just Served Us Nine Pies that is helpful
Wolf, Elissa, Wong, Heidi
core   +1 more source

MAPA: A Semantic Network Framework for Functional Module Discovery and Interpretation in Multi‐Omics Data

Advanced Science, EarlyView.
MAPA transforms complex multi‐omics data into biologically coherent functional modules by integrating pathway information with molecular interaction networks. Retrieval‐augmented large language models then generate structured, literature‐informed interpretations.
Yifei Ge, Feifan Zhang, Yijiang Liu, Chao Jiang, Peng Gao, Nguan Soon Tan, Sai Zhang, Yuchen Shen, Qianyi Zhou, Xin Zhou, Xiao Wang, Fangqing Zhao, Chuchu Wang, Xiaotao Shen   +13 more
wiley   +1 more source

Evaluating Synthetic Sentence Coherence Using a Large Language Model

Proceedings of the International Florida Artificial Intelligence Research Society Conference
Fine-tuning a Large Language Model (LLM) to translate imprecise, ambiguous natural language into a formal logic language that supports automated reasoning requires a significant amount of training data.
Richard Thompson, Angelos Toutsios, Adam Pease, Mathias Kölsch, Christian Darken   +4 more
doaj  

A Zero-Shot Explainable Doctor Ranking Framework with Large Language Models

Big Data Mining and Analytics
Online medical service provides patients with convenient access to doctors, but effectively ranking doctors based on specific medical needs remains challenging.
Ziyang Zeng, Dongyuan Li, Yuqing Yang
doaj   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Large Language Model (LLM)-Enabled Graphs in Dynamic Networking

IEEE Network
Recent advances in generative artificial intelligence (AI), and particularly the integration of large language models (LLMs), have had considerable impact on multiple domains. Meanwhile, enhancing dynamic network performance is a crucial element in promoting technological advancement and meeting the growing demands of users in many applications areas ...
Geng Sun 0001, Yixian Wang, Dusit Niyato, Jiacheng Wang 0001, Xinying Wang, H. Vincent Poor, Khaled B. Letaief   +6 more
openaire   +2 more sources

Automated Radiology Report Labeling in Chest X-Ray Pathologies: Development and Evaluation of a Large Language Model Framework

JMIR Medical Informatics
BackgroundLabeling unstructured radiology reports is crucial for creating structured datasets that facilitate downstream tasks, such as training large-scale medical imaging models.
Abdullah Abdullah, Seong Tae Kim
doaj   +1 more source

Determinants of Knowledge and Usage of Generative Artificial Intelligence in Agricultural Extension: Evidence From Tennessee Extension Personnel

Agribusiness, EarlyView.
ABSTRACT This paper examines the determinants of generative AI (GenAI) knowledge and usage among agricultural extension professionals. Drawing on survey data from agricultural extension personnel in Tennessee, we employ regression analyses and latent Dirichlet allocation (LDA) for topic modeling of open‐ended responses to study the knowledge and usage ...
Abdelaziz Lawani, Jason de Koff, James Muriuki, Anna‐Liisa Ihuhwa   +3 more
wiley   +1 more source

Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs

ACM SIGKDD Explorations Newsletter
Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic ...
Zhikai Chen, Haitao Mao, Hang Li 0007, Wei Jin 0009, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin 0001, Wenqi Fan, Hui Liu 0031, Jiliang Tang   +10 more
openaire   +2 more sources

A large language model is structured like the unconscious: the (ordinary) perverse psychosis of AI

This is an author's accepted manuscript of an article publshed in The European Journal of Psychoanalysis on 11 November 2025. The published version of the article can be accessed here: https://www.journal-psychoanalysis.eu/articles/a-large-language-model-
Geal, Robert
core   +1 more source