Unifying large language models and knowledge graphs: A roadmap
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 …
field of natural language processing and artificial intelligence, due to their emergent ability …
Reasoning on graphs: Faithful and interpretable large language model reasoning
Large language models (LLMs) have demonstrated impressive reasoning abilities in
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …
Knowledgeable preference alignment for llms in domain-specific question answering
Recently, the development of large language models (LLMs) has attracted wide attention in
academia and industry. Deploying LLMs to real scenarios is one of the key directions in the …
academia and industry. Deploying LLMs to real scenarios is one of the key directions in the …
Memory injections: Correcting multi-hop reasoning failures during inference in transformer-based language models
Answering multi-hop reasoning questions requires retrieving and synthesizing information
from diverse sources. Large Language Models (LLMs) struggle to perform such reasoning …
from diverse sources. Large Language Models (LLMs) struggle to perform such reasoning …
Chatkbqa: A generate-then-retrieve framework for knowledge base question answering with fine-tuned large language models
Knowledge Base Question Answering (KBQA) aims to derive answers to natural language
questions over large-scale knowledge bases (KBs), which are generally divided into two …
questions over large-scale knowledge bases (KBs), which are generally divided into two …
Kg-gpt: A general framework for reasoning on knowledge graphs using large language models
While large language models (LLMs) have made considerable advancements in
understanding and generating unstructured text, their application in structured data remains …
understanding and generating unstructured text, their application in structured data remains …
Reasoninglm: Enabling structural subgraph reasoning in pre-trained language models for question answering over knowledge graph
Question Answering over Knowledge Graph (KGQA) aims to seek answer entities for the
natural language question from a large-scale Knowledge Graph~(KG). To better perform …
natural language question from a large-scale Knowledge Graph~(KG). To better perform …
Retrieval-enhanced knowledge editing for multi-hop question answering in language models
Large Language Models (LLMs) have shown proficiency in question-answering tasks but
often struggle to integrate real-time knowledge updates, leading to potentially outdated or …
often struggle to integrate real-time knowledge updates, leading to potentially outdated or …
A review of graph neural networks and pretrained language models for knowledge graph reasoning
J Ma, B Liu, K Li, C Li, F Zhang, X Luo, Y Qiao - Neurocomputing, 2024 - Elsevier
Abstract Knowledge Graph (KG) stores human knowledge facts in an intuitive graphical
structure but faces challenges such as incomplete construction or inability to handle new …
structure but faces challenges such as incomplete construction or inability to handle new …
Knowledgenavigator: Leveraging large language models for enhanced reasoning over knowledge graph
T Guo, Q Yang, C Wang, Y Liu, P Li, J Tang… - Complex & Intelligent …, 2024 - Springer
Large language models have achieved outstanding performance on various downstream
tasks with their advanced understanding of natural language and zero-shot capability …
tasks with their advanced understanding of natural language and zero-shot capability …