Recently, ChatGPT, a representative large language model (LLM), has gained considerable
attention. Due to their powerful emergent abilities, recent LLMs are considered as a possible …

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

Large Language Models (LLMs) have garnered considerable interest within both academic
and industrial. Yet, the application of LLMs to graph data remains under-explored. In this …

Complex logical query answering (CLQA) is a recently emerged task of graph machine
learning that goes beyond simple one-hop link prediction and solves a far more complex …

Large Language Models (LLMs) have revolutionized solutions for general natural language
processing (NLP) tasks. However, deploying these models in specific domains still faces …

Knowledge graph reasoning (KGR) answers logical queries over a knowledge graph (KG),
and embedding-based KGR (EKGR) becomes popular recently, which embeds both queries …

A novel framework called TravelRAG is introduced in this paper, which is built upon a large
language model (LLM) and integrates Retrieval-Augmented Generation (RAG) with …

Answering complex logical queries over incomplete knowledge graphs (KGs) is challenging.
Most previous works have focused on learning entity/relation embeddings and simulating …

ABSTRACT Background In recent years, Large Models (LMs) have been rapidly developed,
including large language models, visual foundation models, and multimodal LMs. They are …

Large Language Models (LLMs) have shown unprecedented performance in various real-
world applications. However, they are known to generate factually inaccurate outputs, aka …