A Question Answering Dataset for Temporal-Sensitive Retrieval-Augmented Generation. [PDF]
Chen Z +9 more
europepmc +1 more source
Composition‐dependent structural evolution in GeXSe1‐X selector‐only memory (SOM) is correlated with device switching behavior. Increasing Ge strengthens network rigidity, suppresses atomic motion, and stabilizes threshold switching, while narrowing the memory window. The revealed structure–property relationship provides a guideline for compositionally
Tien Anh Nguyen +9 more
wiley +1 more source
WaterRAG: A Multiagent Retrieval-Augmented Generation Framework to Support Water Industry Transitions to Net-Zero. [PDF]
Zhai M +7 more
europepmc +1 more source
ABSTRACT Nutritional information is very important in the food choices of consumers. However, when they are too scientific or technical, they have the potential to confuse consumers, resulting in information asymmetry and dissuading them from making beneficial choices.
Edeoba W. Edobor +3 more
wiley +1 more source
Integrating Fine-Tuning and Retrieval-Augmented Generation for Healthcare AI Systems: A Scoping Review. [PDF]
Collaco BG +8 more
europepmc +1 more source
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
Designing metaverse interaction systems for the Turkish language enhanced by fine-tuning and retrieval-augmented generation (RAG). [PDF]
Özkal İ, Başçiftçi F.
europepmc +1 more source
Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents
This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer +3 more
wiley +1 more source
Development and evaluation of a large language model-based, retrieval-augmented generation application for query response in early oncology clinical trials. [PDF]
Pesántez D +8 more
europepmc +1 more source
LLM‐Based Scientific Assistants for Knowledge Extraction: Which Design Choices Matter?
A comprehensive framework for optimizing Large Language Models in domain‐specific applications is introduced. The LLM Playground integrates Prompt Engineering, knowledge augmentation, and advanced reasoning strategies to enable systematic comparison of architectures and base models.
David Exler +7 more
wiley +1 more source

