arxiv: 2604.16929 · v1 · submitted 2026-04-18 · 💻 cs.CL

Recognition: unknown

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

Ruijun Huang , Zhiqiao Kang , Yuxuan Zhu , Junxiong Li , Jiahao Zhao , Minghuan Tan , Feng Jiang , Min Yang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 07:18 UTC · model grok-4.3

classification 💻 cs.CL

keywords hallucination mitigationscientific measurement extractionlarge language modelsfine-tuningreward curriculumMeasEval benchmarkAI4Science

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The pith

MeasHalu uses a taxonomy of measurement errors and two-stage fine-tuning with progressive rewards to cut hallucinations when LLMs extract scientific data from papers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper targets the problem of LLMs fabricating or distorting quantitative details such as numbers, units, and relations when reading scientific literature. It builds a specific error taxonomy covering quantities, units, modifiers, and relations, then trains models through staged reasoning supervision on augmented data followed by a reward schedule that targets those error types one at a time. A reader would care because reliable automated extraction could let researchers compile measurements across thousands of papers without constant manual checking, supporting large-scale synthesis in science. The approach claims to deliver lower hallucination rates and higher accuracy on the MeasEval benchmark without needing to retrain from scratch.

Core claim

By first defining a fine-grained taxonomy that groups measurement hallucinations into errors on quantities, units, modifiers, and relations, then applying two-stage reasoning-aware fine-tuning on augmented scientific text with process supervision, and finally using a progressive reward curriculum that penalizes each hallucination type in turn, the MeasHalu framework substantially lowers hallucination rates and raises overall accuracy on the MeasEval benchmark.

What carries the argument

The progressive reward curriculum that assigns penalties to specific hallucination categories during staged training, paired with the two-stage reasoning-aware fine-tuning on augmented data.

If this is right

Hallucination rates drop noticeably on measurement extraction tasks from scientific text.
Overall accuracy rises on the MeasEval benchmark for pulling quantities and units.
Automated systems for compiling quantitative findings from literature become more dependable.
Large-scale machine-assisted analysis of research papers gains trustworthiness.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same staged-reward idea could be adapted to reduce hallucinations in other narrow extraction domains such as chemical formulas or biological sequences.
Models trained this way should be checked on unrelated general-knowledge tasks to confirm capability is preserved.
If the taxonomy proves stable, it could serve as a template for building similar error classifications in other scientific subfields.

Load-bearing premise

The taxonomy captures the main error types that occur in measurement extraction and the training procedure reduces those errors without introducing new biases or eroding the model's general capabilities.

What would settle it

Evaluate the trained model on a fresh collection of scientific papers containing diverse measurement expressions and find that hallucination rates stay the same as or exceed the untreated baseline model.

Figures

Figures reproduced from arXiv: 2604.16929 by Feng Jiang, Jiahao Zhao, Junxiong Li, Minghuan Tan, Min Yang, Ruijun Huang, Yuxuan Zhu, Zhiqiao Kang.

**Figure 1.** Figure 1: Motivation of MeasHalu. To rectify parsing failures, we propose a taxonomy-based approach to mitigate quantity and relation hallucinations. base construction, and autonomous scientific discovery (Hanson et al., 2024; Chen et al., 2025). Central to this process is scientific measurement extraction—the task of identifying numerical quantities, their units, modifiers, and their relationships to measured en… view at source ↗

**Figure 2.** Figure 2: Overview of our method consisting of two stages, Supervised Fine-Tuning & GRPO based Reinforcement [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Comparison of sentence-based and rule-based reasoning approaches [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MeasHalu gives a targeted taxonomy for measurement hallucinations plus a two-stage fine-tuning recipe that improves MeasEval scores, but lacks any checks for capability loss or generalization beyond that benchmark.

read the letter

The main takeaway is that this paper defines a fine-grained taxonomy of measurement hallucinations (quantities, units, modifiers, relations) and pairs it with a two-stage reasoning-aware fine-tuning method plus a progressive reward curriculum. It shows gains on the MeasEval benchmark for scientific measurement extraction. That combination is new enough to stand out from generic hallucination work. They do a good job focusing on a concrete, high-stakes failure mode in AI4Science: LLMs pulling wrong numbers and units from papers. The taxonomy is practical and the training steps try to add process supervision rather than just outcome matching, which is a reasonable direction. The abstract claims substantial reductions in hallucination rates, and the approach looks like honest engineering on a real bottleneck. The soft spot is the evaluation. All reported improvements are on MeasEval alone. There are no ablations on held-out scientific domains, no tests on standard capability benchmarks to check for general reasoning loss, and no measurement of whether the reward curriculum creates new biases on non-measurement tasks. Without those controls it is hard to know if the method is robust or just tuned to this one distribution. This paper is for researchers working on reliable quantitative extraction from scientific literature. A reader building tools for automated literature analysis would get concrete ideas from the taxonomy and training details. It deserves peer review because the problem matters and the method is specific, even though reviewers will probably require broader validation experiments.

Referee Report

1 major / 2 minor

Summary. The paper claims that MeasHalu mitigates scientific measurement hallucinations in LLMs via a fine-grained taxonomy of errors (quantities, units, modifiers, relations), a two-stage reasoning-aware fine-tuning process using augmented data and process supervision, and a progressive reward curriculum that penalizes specific hallucination types. It reports that this substantially reduces hallucination rates and improves accuracy on the MeasEval benchmark.

Significance. If the empirical gains hold under broader testing, the work would provide a practical, targeted method for improving faithfulness in automated extraction of quantitative findings from scientific literature, addressing a recognized bottleneck in AI4Science pipelines for large-scale knowledge integration.

major comments (1)

[§4] §4 (Experimental Results): All reported gains are confined to the MeasEval benchmark. To substantiate the broader claim of reliable mitigation 'without new biases or loss of general capability,' the manuscript must include ablations on held-out scientific domains, evaluations on standard capability suites (e.g., MMLU or GSM8K), and checks for systematic biases on non-measurement tasks; absent these, the results risk overfitting to the benchmark distribution and do not yet support the general assertion.

minor comments (2)

[Abstract] Abstract: The high-level claims lack any quantitative metrics, error bars, or dataset statistics, which hinders immediate assessment of the magnitude of improvement.
[§2] §2 (Taxonomy): While the four-category taxonomy is introduced, concrete examples of each hallucination type with model outputs would improve clarity and allow readers to verify coverage.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and recommendation for major revision. We address the single major comment point by point below, agreeing that the evaluation scope requires expansion to better support the manuscript's claims.

read point-by-point responses

Referee: [§4] §4 (Experimental Results): All reported gains are confined to the MeasEval benchmark. To substantiate the broader claim of reliable mitigation 'without new biases or loss of general capability,' the manuscript must include ablations on held-out scientific domains, evaluations on standard capability suites (e.g., MMLU or GSM8K), and checks for systematic biases on non-measurement tasks; absent these, the results risk overfitting to the benchmark distribution and do not yet support the general assertion.

Authors: We agree that the reported results are confined to the MeasEval benchmark and that this constrains support for claims about the absence of new biases or loss of general capability. The manuscript's primary focus is the targeted mitigation of measurement hallucinations in scientific extraction, but we recognize the need for broader validation to rule out overfitting. In the revised manuscript, we will add: (1) ablations on held-out scientific domains using papers from additional sources (e.g., recent arXiv submissions in physics and biology with no overlap to MeasEval training data); (2) evaluations on standard capability benchmarks including MMLU and GSM8K to assess retention of general knowledge and reasoning; and (3) checks for systematic biases on non-measurement tasks such as general QA and summarization. These changes will directly address the risk of benchmark-specific overfitting. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical framework without self-referential derivations

full rationale

The paper presents a taxonomy of measurement hallucinations, a two-stage reasoning-aware fine-tuning approach with augmented data and process supervision, plus a progressive reward curriculum, all evaluated empirically on the MeasEval benchmark. No equations, derivations, or mathematical claims are present that could reduce outputs to inputs by construction. The central results are reported performance improvements rather than any 'prediction' forced by fitted parameters or self-definitional loops. Self-citations, if any, do not serve as load-bearing justification for uniqueness or ansatz choices. The work is self-contained as standard applied ML methodology on a new task, with no reduction of the reported gains to the training setup itself.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the unverified effectiveness of the proposed taxonomy and training strategy for reducing measurement-specific hallucinations. No free parameters, physical constants, or new entities are mentioned in the abstract.

axioms (2)

domain assumption Large language models can be fine-tuned with augmented data and process supervision to reduce domain-specific hallucinations.
The two-stage strategy assumes standard LLM training techniques transfer effectively to measurement extraction.
ad hoc to paper A fine-grained taxonomy of errors can be used to guide targeted optimization via rewards.
The taxonomy is introduced by the paper as the basis for the reward curriculum.

pith-pipeline@v0.9.0 · 5497 in / 1305 out tokens · 54455 ms · 2026-05-10T07:18:07.964283+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

7 extracted references · 3 canonical work pages

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Let’s verify step by step. InThe Twelfth Inter- national Conference on Learning Representations. Zhiyuan Liu, Yaorui Shi, An Zhang, Sihang Li, Enzhi Zhang, Xiang Wang, Kenji Kawaguchi, and Tat-Seng Chua. 2024. ReactXT: Understanding molecular “reaction-ship” via reaction-contextualized molecule- text pretraining. InFindings of the Association for Computat...

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Tarek Saier, Mayumi Ohta, Takuto Asakura, and Michael Färber

Direct preference optimization: Your language model is secretly a reward model.Advances in neural information processing systems, 36:53728–53741. Tarek Saier, Mayumi Ohta, Takuto Asakura, and Michael Färber. 2024. Hyperpie: Hyperparameter in- formation extraction from scientific publications. In Advances in Information Retrieval, pages 254–269, Cham. Spri...

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Output Format (TSV Fields):

Example Process: . . . Output Format (TSV Fields): . . . Final Output Example: . . . The reference answer from quantulum:. . . Prompt forP aug Instruction: You are an expert in extracting structured annotations from text. I have an text input and you need to extract all the quantities within it. I need you to strictly follow the format with six specific s...
[7]

Annotation of Quantities:
[8]

Fig. 4”) or scientific nomenclature con- taining digits (e.g., “4S RNA

Example Process: . . . Output Format (TSV Fields): . . . Final Output Example: . . . The gold answers:. . . B Case Study: High-Entropy Token Suppression by GRPO To better understand the effect of GRPO, we exam- ine a representative sample: Input: Samples were then annealed in air in a pre-heated furnace at temperatures up to 798 °C for times chosen to ens...

work page arXiv 2021