Back to feed
arXiv cs.CL
arXiv cs.CL
6/19/2026
Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning

Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning

Short summary

Researchers introduce LUCID, a new method to detect hallucinations in large language models used for knowledge graph reasoning. It combines LLM attention scores, knowledge graph semantics, and structural information via graph neural networks. Testing on nine datasets shows state-of-the-art performance compared to 15 existing methods.

  • •LUCID: novel hallucination detection for LLM-based knowledge graph reasoning
  • •Combines attention scores, KG semantics, and structural information using GNNs
  • •Achieves state-of-the-art on 9 datasets vs. 15 baselines

Generated with AI, which can make mistakes.

#research-breakthrough#ai-tools#ai-agents
Read full article at arXiv cs.CL

Is this a good recommendation for you?

Explore more

Sanity Checks for Long-Form Hallucination Detection
arXiv cs.CL
Holographic Memory for Zero-Shot Compositional Reasoning in Knowledge Graphs: A Mechanistic Study of Where and Why It Fails
arXiv cs.LG
CoRA: Confidence-Rationale Alignment for Reliable Chain-of-Thought Reasoning
arXiv cs.CL
CIExplainer++: Generating Causal and Interpretable Explanations for Graph Neural Networks
arXiv cs.LG