MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning
This paper presents a new method to improve how AI systems extract scientific measurements from research papers, addressing common errors that can lead to incorrect conclusions.
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- 1 Across all baseline LLMs, sentence-based prompting consistently outperforms rule-based prompting (e.g., Gemini-2.5-Pro improves from 0.359 to 0.440), supporting our hypothesis that sentence-level localized reasoning is more effective than rigid global rule-based deduction for complex quantitative relation extraction.
- 2 To further understand the contribution of each reward component, we conduct fine-grained ablation studies by removing individual reward terms from the GRPO objective.
- 3 Ablation on Reward Components To further investigate the contribution of each reward component for relation-based hallucination mitigation, we conduct fine-grained ablation studies by removing individual reward terms from the GRPO objective.
- 4 A penalty is triggered if the extracted string fails to be parsed as a valid physical or numerical quantity, preventing the model from inventing nonsensical values.
Introduction
These quantitative statements form the evidential backbone of experimental sciences across disciplines ranging from materials science to biomedical research . A key challenge underlying this failure is that measurement hallucinations differ fundamentally from general textual hallucinations.
Unlike opendomain factual errors, measurement hallucinations exhibit fine-grained structural failures: models fab-ricate nonexistent values, misassociate quantities with wrong entities, overlook crucial qualifiers, or distort relations between scientific variables .
Existing hallucination mitigation techniques, such as retrieval augmentation , generic instruction tuning, or conversational verification , remain insufficient, as they are not designed to enforce the strict grounding and structural consistency required by scientific measurements.
Using only gold-standard data (w/o (D aug + GRPO)) gives the lowest scores (e.g., 0.346 for 3B), showing models cannot capture complex multidomain quantitative annotation rules without prior measurement extraction schema scaffolding.
First, even though MeasHalu outperforms all existing baselines, the extraction performance for sparse components (e.g., qualifiers, F1 = 0.170) remains low, hindered by limited annotated data and ambiguous semantic dependencies in scientific text.
Research Question
To further understand the contribution of each reward component, we conduct fine-grained ablation studies by removing individual reward terms from the GRPO objective. Across all baseline LLMs, sentence-based prompting consistently outperforms rule-based prompting (e.g., Gemini-2.5-Pro improves from 0.359 to 0.440), supporting our hypothesis that sentence-level localized reasoning is more effective than rigid global rule-based deduction for complex quantitative relation extraction.
Ablation on Reward Components To further investigate the contribution of each reward component for relation-based hallucination mitigation, we conduct fine-grained ablation studies by removing individual reward terms from the GRPO objective.
Methodology
The rapid expansion of scientific literature has created an unprecedented demand for reliable automatic extraction of quantitative knowledge, which lies at the core of modern AI4Science applications such as large-scale meta-analysis, knowledge base construction, and autonomous scientific discovery . Central to this process is scientific measurement extraction-the task of identifying numerical quantities, their units, modifiers, and their relationships to measured entities and properties.
Study Design
Although recent Large Language Models (LLMs) have demonstrated remarkable generalization abilities, they continue to perform unreliably on this task : even minor hallucinations in quantities or relations can invalidate entire experimental conclusions, severely limiting the trustworthiness of LLM-driven scientific understanding systems.
Yet, despite the importance of this problem, current research lacks both a systematic analysis of measurement-specific hallucination phenomena and targeted learning mechanisms for their mitigation.
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Results & Findings
In this work, we present MEASHALU, a reasoning-enhanced framework that explicitly models and suppresses scientific measurement hallucinations in LLMs. MEASHALU addresses these failure modes through a unified learning pipeline that combines reasoning-aware supervised finetuning with targeted reinforcement learning via structured reward shaping, thereby internalizing scientific grounding constraints directly into model parameters.
- In this work, we present MEASHALU, a reasoning-enhanced framework that explicitly models and suppresses scientific measurement hallucinations in LLMs.
- MEASHALU addresses these failure modes through a unified learning pipeline that combines reasoning-aware supervised finetuning with targeted reinforcement learning via structured reward shaping, thereby internalizing scientific.
- Extensive experiments on the MeasEval benchmark and our newly constructed MeasEval-Ext dataset demonstrate that MEASHALU substantially reduces hallucination rates and consistently outperforms strong supervised baselines and.
- Furthermore, we show that MEASHALU functions as a reliable external measurement extraction tool that significantly improves performance on downstream embodied scientific tasks, validating its practical utility.
- We construct a new out-of-distribution evaluation benchmark, MEASEVAL-EXT, and demonstrate through extensive experiments that MEASHALU substantially reduces hallucination rates and consistently outperforms strong supervised baselines and.
Although MeasEval is a highquality benchmark, its limited size and dated sources underrepresent emerging units.
A penalty is triggered if the extracted string fails to be parsed as a valid physical or numerical quantity, preventing the model from inventing nonsensical values.
Practical Applications
Third, processing ultra-long documents with nested measurement relations may introduce computational inefficiencies, as the sentence-based reasoning strategy requires contextual localization for each quantity.
Hallucinations in Large Language Models
The section elaborates on the types of hallucinations in LLMs, focusing on the need for a taxonomy that addresses the structural requirements of measurement extraction.
General Information Extraction vs. Measurement Extraction
This part contrasts general information extraction with the specific challenges of scientific measurement extraction, emphasizing the need for models to maintain numerical and relational accuracy.
Frequently Asked Questions
To further understand the contribution of each reward component, we conduct fine-grained ablation studies by removing individual reward terms from the GRPO objective. Across all baseline LLMs, sentence-based prompting consistently outperforms rule-based prompting (e.g., Gemini-2.5-Pro improves from 0.359 to 0.440), supporting our.
Central to this process is scientific measurement extraction-the task of identifying numerical quantities, their units, modifiers, and their relationships to measured entities and properties. In this section, we evaluate our relation-based hallucination mitigation method on the MeasEval dataset and MeasEval-Ext, a newly.
A penalty is triggered if the extracted string fails to be parsed as a valid physical or numerical quantity, preventing the model from inventing nonsensical values. To evaluate robustness under distribution shift, we introduce MeasEval-Ext, annotated strictly following the MeasEval schema.
Format compliance reward (r fmt ): A binary reward is assigned for strict adherence to the predefined structure , . . . , , enforcing schema compliance and parsability of generated reasoning chains. It is crucial that you adhere to this structure.
Using only gold-standard data (w/o (D aug + GRPO)) gives the lowest scores (e.g., 0.346 for 3B), showing models cannot capture complex multidomain quantitative annotation rules without prior measurement extraction schema scaffolding. Although MeasEval is a highquality benchmark, its limited size and.
This paper presents a new method to improve how AI systems extract scientific measurements from research papers, addressing common errors that can lead to incorrect conclusions.