HalluMat: Detecting Hallucinations in LLM-Generated Materials Science Content Through Multi-Stage Verification

• URL: http://arxiv.org/abs/2512.22396v1
• Date: Fri, 26 Dec 2025 22:16:12 GMT
• Title: HalluMat: Detecting Hallucinations in LLM-Generated Materials Science Content Through Multi-Stage Verification
• Authors: Bhanu Prakash Vangala, Sajid Mahmud, Pawan Neupane, Joel Selvaraj, Jianlin Cheng,
• Abstract summary: HalluMatData is a benchmark dataset for evaluating hallucination detection methods.<n>HalluMatDetector is a multi-stage hallucination detection framework.<n>HalluMatDetector reduces hallucination verification rates by 30%.
• Score: 0.9490124006642771
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Artificial Intelligence (AI), particularly Large Language Models (LLMs), is transforming scientific discovery, enabling rapid knowledge generation and hypothesis formulation. However, a critical challenge is hallucination, where LLMs generate factually incorrect or misleading information, compromising research integrity. To address this, we introduce HalluMatData, a benchmark dataset for evaluating hallucination detection methods, factual consistency, and response robustness in AI-generated materials science content. Alongside this, we propose HalluMatDetector, a multi-stage hallucination detection framework that integrates intrinsic verification, multi-source retrieval, contradiction graph analysis, and metric-based assessment to detect and mitigate LLM hallucinations. Our findings reveal that hallucination levels vary significantly across materials science subdomains, with high-entropy queries exhibiting greater factual inconsistencies. By utilizing HalluMatDetector verification pipeline, we reduce hallucination rates by 30% compared to standard LLM outputs. Furthermore, we introduce the Paraphrased Hallucination Consistency Score (PHCS) to quantify inconsistencies in LLM responses across semantically equivalent queries, offering deeper insights into model reliability.

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