Results 121 to 130 of about 55,072 (308)

A Review of Applying Large Language Models in Healthcare

IEEE Access
In response to the growing demand for healthcare and the increasing importance people place on medical services, efficiently meeting these needs within the constraints of limited healthcare resources is of great social and economic benefit.
Qiming Liu, Ruirong Yang, Qin Gao, Tengxiao Liang, Xiuyuan Wang, Shiju Li, Bingyin Lei, Kaiye Gao   +7 more
doaj   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source

The Illusion of Explainability with LLMs and LLM-Agents

The formal linguistic capabilities of Large Language Models (LLMs) are increasingly intersecting with the field of Explainable Agency (XAg). The growing adoption of LLM-agents has heightened the need to explain their behaviour to users. However, current methods consistently fail to meet desirable properties of human-centric explanations.
Montese, Sara, Alvarez-Napagao, Sergio, Gimenez-Abalos, Victor, Cortés, Ulises   +3 more
openaire   +3 more sources

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

IEEE Access
Large Language Models (LLMs) recently demonstrated extraordinary capability in various natural language processing (NLP) tasks including language translation, text generation, question answering, etc.
Mohaimenul Azam Khan Raiaan, Md. Saddam Hossain Mukta, Kaniz Fatema, Nur Mohammad Fahad, Sadman Sakib, Most Marufatul Jannat Mim, Jubaer Ahmad, Mohammed Eunus Ali, Sami Azam   +8 more
doaj   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Scheming Ability in LLM-to-LLM Strategic Interactions

CoRR
As large language model (LLM) agents are deployed autonomously in diverse contexts, evaluating their capacity for strategic deception becomes crucial. While recent research has examined how AI systems scheme against human developers, LLM-to-LLM scheming remains underexplored.
openaire   +2 more sources

Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-LLM Interactions

CoRR
As Large Language Models (LLMs) increasingly power applications used by children and adolescents, ensuring safe and age-appropriate interactions has become an urgent ethical imperative. Despite progress in AI safety, current evaluations predominantly focus on adults, neglecting the unique vulnerabilities of minors engaging with generative AI.
Junfeng Jiao, Saleh Afroogh, Kevin Chen, Abhejay Murali, David Atkinson, Amit Dhurandhar   +5 more
openaire   +2 more sources

Knowledge Internalized in LLMs

LLMs, trained on a diverse range of resources, internalize a substantial amount of knowledge. Understanding this internalized knowledge is crucial for assessing the accuracy and reliability of their outputs, guiding improvements in model design, and ...
Tang, Nan, Sun, Kai, Sun, Yushi, Yang, Xiao   +3 more
core   +1 more source

LLMs for Semantic Web Query

, 2023
The emergence of Large Language Models like GPT-4 offers unprecedented capabilities in understanding human intent and generating text. This tutorial explores the intersection of LLMs and semantic web applications, focusing on how these models can ...
Chen, Yinlin
core