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 demonstrated impressive reasoning abilities in
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …

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

To address the issue of insufficient knowledge and the tendency to generate hallucination in
Large Language Models (LLMs), numerous studies have endeavored to integrate LLMs with …

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such as hallucinations and outdated knowledge.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

Large language models (LLMs) have demonstrated impressive reasoning abilities, but they
still struggle with faithful reasoning due to knowledge gaps and hallucinations. To address …

Retrieval-augmented methods are successful in the standard scenario where the retrieval
space is sufficient; whereas in the few-shot scenario with limited retrieval space, this paper …

Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream
task execution by retrieving additional information, such as knowledge, skills, and tools from …

Knowledge graph question answering (KGQA) involves answering natural language
questions by leveraging structured information stored in a knowledge graph. Typically …

Large language models (LLMs) encounter challenges such as hallucination and factual
errors in knowledge-intensive tasks. One the one hand, LLMs sometimes struggle to …