MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning

2026-04-18

AI Summary

This work addresses the challenge of hallucination in large language models when extracting quantitative measurements from scientific literature, a critical issue that undermines the reliability of automated scientific understanding systems. To mitigate this, the authors propose MeasHalu, a novel framework that introduces a fine-grained taxonomy of scientific measurement hallucinations and employs a reasoning-aware two-stage fine-tuning strategy. This approach integrates scientific data augmentation, process supervision, and a progressive reward curriculum to enhance model fidelity. Evaluated on the MeasEval benchmark, MeasHalu substantially reduces hallucination rates while significantly improving overall extraction accuracy, offering a robust solution for trustworthy automated scientific knowledge extraction.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Information Extraction
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The accurate extraction of scientific measurements from literature is a critical yet challenging task in AI4Science, enabling large-scale analysis and integration of quantitative research findings. However, Large Language Models (LLMs) frequently exhibit severe hallucinations, which significantly undermine the reliability of automated scientific document understanding systems. To address this problem, we propose MeasHalu, a novel framework for mitigating scientific measurement hallucinations through enhanced reasoning and targeted optimization. We first present a fine-grained taxonomy of measurement-specific hallucinations, categorizing errors across quantities, units, modifiers, and relations. Our approach incorporates a two-stage reasoning-aware fine-tuning strategy using augmented scientific data and process-based supervision. Furthermore, we introduce a progressive reward curriculum designed to penalize specific hallucination types, significantly improving extraction faithfulness. Experimental results demonstrate that MeasHalu substantially reduces hallucination rates and improves overall accuracy on the MeasEval benchmark. This work provides a targeted solution to a key bottleneck in automated scientific knowledge extraction, facilitating more trustworthy and scalable machine-assisted scientific literature analysis.

Problem

• scientific measurement hallucinations
• Large Language Models
• AI4Science
• automated scientific knowledge extraction
• measurement extraction

Innovation

• scientific measurement hallucination
• reasoning-aware fine-tuning
• reward curriculum
• AI4Science
• MeasHalu