The adoption of large language models (LLMs) in healthcare demands a careful analysis of
their potential to spread false medical knowledge. Because LLMs ingest massive volumes of …

Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in
addressing the challenges of Large Language Models (LLMs) without necessitating …

The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …

Recent advancements in large language models (LLMs) have achieved promising
performances across various applications. Nonetheless, the ongoing challenge of …

Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations
such as hallucinations and outdated knowledge. Knowledge Graph (KG)-based Retrieval …

Graphs play an important role in representing complex relationships in various domains like
social networks, knowledge graphs, and molecular discovery. With the advent of deep …

Large Language Models (LLMs) have shown exceptional abilities for multiple different
natural language processing tasks. While prompting is a crucial tool for LLM inference, we …

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 …

Attributed Question Answering (AQA) aims to provide both a trustworthy answer and a
reliable attribution report for a given question. Retrieval is a widely adopted approach …

Large language models present opportunities for innovative Question Answering over
Knowledge Graphs (KGQA). However, they are not inherently designed for query …