Give us the facts: Enhancing large language models with knowledge graphs for fact-aware language modeling

L Yang, H Chen, Z Li, X Ding… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
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 on graphs: A comprehensive survey

B **, G Liu, C Han, M Jiang, H Ji… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …

Unifying large language models and knowledge graphs: A roadmap

S Pan, L Luo, Y Wang, C Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
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 …

Retrieval-augmented generation for ai-generated content: A survey

P Zhao, H Zhang, Q Yu, Z Wang, Y Geng, F Fu… - arxiv preprint arxiv …, 2024 - arxiv.org
The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …

G-retriever: Retrieval-augmented generation for textual graph understanding and question answering

X He, Y Tian, Y Sun, N Chawla… - Advances in …, 2025 - proceedings.neurips.cc
Given a graph with textual attributes, we enable users tochat with their graph': that is, to ask
questions about the graph using a conversational interface. In response to a user's …

Reasoning on graphs: Faithful and interpretable large language model reasoning

L Luo, YF Li, G Haffari, S Pan - arxiv preprint arxiv:2310.01061, 2023 - arxiv.org
Large language models (LLMs) have demonstrated impressive reasoning abilities in
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …

Chatkbqa: A generate-then-retrieve framework for knowledge base question answering with fine-tuned large language models

H Luo, Z Tang, S Peng, Y Guo, W Zhang, C Ma… - arxiv preprint arxiv …, 2023 - arxiv.org
Knowledge Base Question Answering (KBQA) aims to answer natural language questions
over large-scale knowledge bases (KBs), which can be summarized into two crucial steps …

Retrieval-augmented generation with knowledge graphs for customer service question answering

Z Xu, MJ Cruz, M Guevara, T Wang… - Proceedings of the 47th …, 2024 - dl.acm.org
In customer service technical support, swiftly and accurately retrieving relevant past issues is
critical for efficiently resolving customer inquiries. The conventional retrieval methods in …

Penetrative ai: Making llms comprehend the physical world

H Xu, L Han, Q Yang, M Li, M Srivastava - Proceedings of the 25th …, 2024 - dl.acm.org
Recent developments in Large Language Models (LLMs) have demonstrated their
remarkable capabilities across a range of tasks. Questions, however, persist about the …

Graph retrieval-augmented generation: A survey

B Peng, Y Zhu, Y Liu, X Bo, H Shi, C Hong… - arxiv preprint arxiv …, 2024 - arxiv.org
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in
addressing the challenges of Large Language Models (LLMs) without necessitating …