Results 31 to 40 of about 89,825 (246)

Constituency Parsing using LLMs [PDF]

, 2023
Constituency parsing is a fundamental yet unsolved natural language processing task. In this paper, we explore the potential of recent large language models (LLMs) that have exhibited remarkable performance across various domains and tasks to tackle this
Bai, Xuefeng, Wang, Zhongqing, Zhang, Yue, Wu, Jialong, Chen, Yulong   +4 more
core  

Testimony by LLMs

AI & SOCIETY
Abstract Artificial testimony generated by large language models (LLMs) can be a source of knowledge. However, the requirement that artificial testifiers must satisfy for successful knowledge acquisition is different from the requirement that human testifiers must satisfy.
Jinhua He, Chen Yang
openaire   +2 more sources

Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Epilepsy surgery is severely underutilized despite proven efficacy, with substantial under‐referral of eligible patients in routine clinical practice. This study evaluated the potential role of large language models (LLMs) as decision‐support tools for screening unstructured clinical notes to identify epilepsy surgery candidates and ...
Uriel Fennig, Nadav Amir, Maya Schiller, Roni Loebenstein, Johnatan Nissan, Marina Boxer, Shany Guly Gofrit, Gal Goshen, Sándor Beniczky, Nicola Maggio   +9 more
wiley   +1 more source

LLMs are Introvert

CoRR
The exponential growth of social media and generative AI has transformed information dissemination, fostering connectivity but also accelerating the spread of misinformation. Understanding information propagation dynamics and developing effective control strategies is essential to mitigate harmful content. Traditional models, such as SIR, provide basic
Litian Zhang, Xiaoming Zhang, Bingyu Yan, Ziyi Zhou 0003, Bo Zhang, Zhenyu Guan, Xi Zhang 0008, Chaozhuo Li   +7 more
openaire   +2 more sources

Engagement Patterns With an Artificial Intelligence Health Coach for Systemic Sclerosis Self‐Management: A Mixed Methods Study

Arthritis Care &Research, EarlyView.
Objective To evaluate utility of an artificial intelligence (AI) health coach for systemic sclerosis (SSc) self‐management and identify patterns associated with participant engagement. Methods We conducted a mixed methods study in which an AI health coach, powered by a large language model (LLM), was used to support self‐management for SSc.
Nirali Shah, Melanie Morris, Cristina Daraban, Sophia Johnson, Susan L. Murphy   +4 more
wiley   +1 more source

Patients improve, patterns persist: longitudinal stability of RA joint involvement patterns

Arthritis Care &Research, Accepted Article.
Objective Rheumatoid arthritis is a heterogeneous disease. Data‐driven approaches, from synovial histology to joint involvement patterns (JIPs), have sought to define clinically meaningful subgroups. Whether these subgroups represent stable phenotypes or transient disease states remains unclear.
Tjardo Maarseveen, Yasuo Nagafuchi, Kristina Lend, Seyam Alemyar, Herman Kasper Glas, Gerdur Gröndal, Bjorn Gudbjornsson, Espen A. Haavardsholm, Kim Hørslev Petersen, Jon Lampa, Merete Lund Hetland, Dan Nordström, Mike Nurmohamed, Mikkel Østergaard, Anna Rudin, Marte Schrumpf Heiberg, Tuulikki Sokka‐Isler, Till Uhlig, Josien Veris‐van Dieren, Ronald van Vollenhoven, Tom Huizinga, Marcel JT Reinders, Sytske Anne Bergstra, Erik van den Akker, Rachel Knevel   +24 more
wiley   +1 more source

Generative AI in clinical practice: how should rheumatologists evaluate and monitor emerging tools?

Arthritis Care &Research, Accepted Article.
Generative artificial intelligence (AI) is increasingly being used in rheumatology practice, to assist with tasks such as documentation, record summarization, and clinical decision support. Rheumatologists need a practical approach to determining what evidence and oversight each application requires.
Koushan Kouranloo, Mrinalini Dey, Jinoos Yazdany   +2 more
wiley   +1 more source

Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Advanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang, Hao Li   +3 more
wiley   +1 more source

Benchmarking LLMs via Uncertainty Quantification [PDF]

The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods.
Pang, Jianhui, Wang, Longyue, Wong, Derek F., Shi, Shuming, Tu, Zhaopeng, Yang, Mingming, Ye, Fanghua, Yilmaz, Emine   +7 more
core   +3 more sources

An Epigenetic Fate Converter Based on Nuclei‐Targeting Lipid Nanoparticles Drives Neuronal Programming of Stem Cells for Spinal Cord Repair

Advanced Functional Materials, EarlyView.
Schematic illustration of LNP‐MPG nuclei‐targeting delivery of HMW‐FGF2 promoting histone acetylation to regulate the fate of DPSCs and treat spinal cord injury. LNPs components include pHMW‐FGF2 plasmid, DSPC, Dlin‐MC3‐DMA, cholesterol, and PEG2000, and are modified with MPG to form HMW‐FGF2@LNP‐MPG (HLM). HLM nuclei‐targets DPSCs to deliver HMW‐FGF2,
Heng Zhou, Qiang Qin, Xiqin Li, Hanyu Ding, Chun Xu, Yan He, Qingsong Ye   +6 more
wiley   +1 more source