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 …
Chatkbqa: A generate-then-retrieve framework for knowledge base question answering with fine-tuned large language models
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 …
over large-scale knowledge bases (KBs), which can be summarized into two crucial steps …
Graph retrieval-augmented generation: A survey
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in
addressing the challenges of Large Language Models (LLMs) without necessitating …
addressing the challenges of Large Language Models (LLMs) without necessitating …
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 …
Knowledgeable preference alignment for llms in domain-specific question answering
Deploying large language models (LLMs) to real scenarios for domain-specific question
answering (QA) is a key thrust for LLM applications, which poses numerous challenges …
answering (QA) is a key thrust for LLM applications, which poses numerous challenges …
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 …
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 …
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 …
GS-CBR-KBQA: Graph-structured case-based reasoning for knowledge base question answering
Abstract Knowledge Base Question Answering (KBQA) task is an important research
direction in natural language processing. Due to the flexibility and ambiguity of natural …
direction in natural language processing. Due to the flexibility and ambiguity of natural …