In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-
Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as …

Large language models (LLMs) have demonstrated remarkable capabilities in various
scientific domains, from natural language processing to complex problem-solving tasks …

Large Language Models (LLMs) are pre-trained on large-scale corpora and excel in
numerous general natural language processing (NLP) tasks, such as question answering …

Breast cancer is one of the most common malignant tumors in women worldwide. Although
large language models (LLMs) can provide breast cancer nursing care consultation …

Biomedical knowledge graphs (BKGs) have emerged as powerful tools for organizing and
leveraging the vast and complex data found across the biomedical field. Yet, current reviews …

Large Language Models offer new opportunities to devise automated implementation
generation methods that can tackle problem solving activities beyond traditional methods …

The rapid development of artificial intelligence has brought about substantial advancements
in the field. One promising direction is the integration of Large Language Models (LLMs) …

Interdisciplinary, transdisciplinary, convergence, and No-Boundary Thinking (NBT) research
are methodology and technology-agnostic approaches to problem solving. The focus is on …

Motivation LLMs like GPT-4, despite their advancements, often produce hallucinations and
struggle with integrating external knowledge effectively. While Retrieval-Augmented …

The increasing demand for large-scale language models (LLMs) has highlighted the
importance of efficient data retrieval mechanisms. Neural graph databases (NGDBs) have …