Dynamic few-shot learning for knowledge graph question answering
Large language models present opportunities for innovative Question Answering over
Knowledge Graphs (KGQA). However, they are not inherently designed for query …
Knowledge Graphs (KGQA). However, they are not inherently designed for query …
CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering
Recent studies have explored the use of Large Language Models (LLMs) with Retrieval
Augmented Generation (RAG) for Knowledge Graph Question Answering (KGQA). They …
Augmented Generation (RAG) for Knowledge Graph Question Answering (KGQA). They …
Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments
Large Language Models (LLMs) have shown potential in reasoning over structured
environments, eg, knowledge graph and table. Such tasks typically require multi-hop …
environments, eg, knowledge graph and table. Such tasks typically require multi-hop …
Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs
Knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL)
queries in a low-dimensional KG space for complex reasoning over incomplete KGs. To …
queries in a low-dimensional KG space for complex reasoning over incomplete KGs. To …
Graph Neural Network Enhanced Retrieval for Question Answering of LLMs
Retrieval augmented generation has revolutionized large language model (LLM) outputs by
providing factual supports. Nevertheless, it struggles to capture all the necessary knowledge …
providing factual supports. Nevertheless, it struggles to capture all the necessary knowledge …
A Framework of Knowledge Graph-Enhanced Large Language Model Based on Question Decomposition and Atomic Retrieval
Abstract Knowledge graphs (KGs) can provide explainable reasoning for large language
models (LLMs), alleviating their hallucination problem. Knowledge graph question …
models (LLMs), alleviating their hallucination problem. Knowledge graph question …
Augmenting Reasoning Capabilities of LLMs with Graph Structures in Knowledge Base Question Answering
Recently, significant progress has been made in employing Large Language Models (LLMs)
for semantic parsing to address Knowledge Base Question Answering (KBQA) tasks …
for semantic parsing to address Knowledge Base Question Answering (KBQA) tasks …
SymAgent: A Neural-Symbolic Self-Learning Agent Framework for Complex Reasoning over Knowledge Graphs
B Liu, J Zhang, F Lin, C Yang, M Peng, W Yin - THE WEB CONFERENCE … - openreview.net
Recent advancements have highlighted that Large Language Models (LLMs) are prone to
hallucinations when solving complex reasoning problems, leading to erroneous results. To …
hallucinations when solving complex reasoning problems, leading to erroneous results. To …