Lie to Me: Knowledge Graphs for Robust Hallucination Self-Detection in LLMs

• URL: http://arxiv.org/abs/2512.23547v1
• Date: Mon, 29 Dec 2025 15:41:13 GMT
• Title: Lie to Me: Knowledge Graphs for Robust Hallucination Self-Detection in LLMs
• Authors: Sahil Kale, Antonio Luca Alfeo,
• Abstract summary: We examine the use of structured knowledge representations, namely knowledge graphs, to improve hallucination self-detection.<n>Our results show that LLMs can better analyse atomic facts when they are structured as knowledge graphs.<n>This low-cost, model-agnostic approach paves the way toward safer and more trustworthy language models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Hallucinations, the generation of apparently convincing yet false statements, remain a major barrier to the safe deployment of LLMs. Building on the strong performance of self-detection methods, we examine the use of structured knowledge representations, namely knowledge graphs, to improve hallucination self-detection. Specifically, we propose a simple yet powerful approach that enriches hallucination self-detection by (i) converting LLM responses into knowledge graphs of entities and relations, and (ii) using these graphs to estimate the likelihood that a response contains hallucinations. We evaluate the proposed approach using two widely used LLMs, GPT-4o and Gemini-2.5-Flash, across two hallucination detection datasets. To support more reliable future benchmarking, one of these datasets has been manually curated and enhanced and is released as a secondary outcome of this work. Compared to standard self-detection methods and SelfCheckGPT, a state-of-the-art approach, our method achieves up to 16% relative improvement in accuracy and 20% in F1-score. Our results show that LLMs can better analyse atomic facts when they are structured as knowledge graphs, even when initial outputs contain inaccuracies. This low-cost, model-agnostic approach paves the way toward safer and more trustworthy language models.

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