2026-06-19原文2 min readUpdated: 2026-06-19

Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning

Proposes LUCID, a hallucination detection method that leverages LLM attention scores, KG semantics, and structural information via graph neural networks, achieving state-of-the-art performance on nine datasets.

SourcearXiv Computational LinguisticsAuthor: Xinyan Zhu, Yaoqi Liu, Yue Gao, Huadong Ma, Cheng Yang, Chuan Shi

[2606.19351] Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning

[Submitted on 27 Apr 2026]

Title:Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning

View a PDF of the paper titled Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning, by Xinyan Zhu and 5 other authors

View PDF HTML (experimental)

Abstract:Knowledge graph (KG) reasoning infers new knowledge from existing facts and is widely applied in question answering, recommendation, and decision support. With the rapid development of large language models (LLMs), LLM-based KG reasoning frameworks have become increasingly popular by leveraging retrieved KG information. However, hallucinations in LLMs remain a critical issue. Even when relevant KG knowledge is incorporated, models may still generate incorrect outputs, leading to misinformation and unreliable decisions. Existing hallucination detection methods either focus on LLM internal states or verify consistency with retrieved contexts, but both overlook the structural information in KGs, resulting in suboptimal performance. To address this gap, we propose LUCID, the first halLUcination deteCtIon method for LLM-based knowleDge graph reasoning frameworks. LUCID jointly leverages LLM attention scores, KG semantics, and structural information. Specifically, it extracts node and edge features from attention scores and semantic similarities, and integrates them with KG structure using a graph neural network. We also construct manually annotated benchmark datasets for evaluation. Experiments on nine datasets show that LUCID achieves state of the art performance compared to 15 baselines.

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

Cite as: arXiv:2606.19351 [cs.CL]

(or arXiv:2606.19351v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.19351

arXiv-issued DOI via DataCite

Submission history

From: Xinyan Zhu [view email] [v1] Mon, 27 Apr 2026 12:20:28 UTC (773 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning, by Xinyan Zhu and 5 other authors

View PDF

HTML (experimental)

TeX Source

view license

Current browse context:

cs.CL

new | recent | 2026-06

Change to browse by:

cs cs.AI

References & Citations

NASA ADS

Google Scholar

Semantic Scholar

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)