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

• URL: http://arxiv.org/abs/2604.16929v1
• Date: Sat, 18 Apr 2026 09:26:52 GMT
• Title: MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning
• Abstract summary: MeasHalu is a framework for mitigating scientific measurement hallucinations.<n>MeasHalu substantially reduces hallucination rates and improves overall accuracy on the MeasEval benchmark.
• Score: 16.93649712632818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Tracing the Cascade: A Topology-Aware Evaluation Framework for Scientific Agent Hallucinations [27.330265411994258]
  SCHEMA is an evidence-grounded, topology-aware evaluation framework for hallucinations in scientific agents.<n>We show that hallucinations concentrate at a small set of highly connected knowledge hubs.<n>Results indicate that for high-stakes scientific applications, terminal accuracy alone is an insufficient signal of agent reliability.
  arXiv  Detail & Related papers  (2026-08-01T15:19:10Z)
• Finetuning with Scientific Data Increases Hallucinations: A Multi-domain Factuality Evaluation of LLMs [8.700507644617964]
  Large language models (LLMs) are increasingly used to communicate and explain scientific concepts.<n>We evaluate 18 LLMs by comparing each scientifically fine-tuned model against its general-purpose base.
  arXiv  Detail & Related papers  (2026-06-19T12:03:02Z)
• A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization [14.654160505140426]
  We introduce Elhuyar, a multi-agent, human-in-the-loop system that integrates AI, large language models, and human scientists.<n>The framework distributes tasks among specialized agents for filtering papers, extracting data, fitting models, and summarizing findings, with human oversight ensuring reliability.<n>It generates structured reports with extracted data, visualizations, model equations, and text summaries, enabling deeper inquiry through iterative refinement.
  arXiv  Detail & Related papers  (2026-04-01T22:58:42Z)
• CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era [51.63024682584688]
  Large language models (LLMs) introduce a new risk: fabricated references that appear plausible but correspond to no real publications.<n>We present the first comprehensive benchmark and detection framework for hallucinated citations in scientific writing.<n>Our framework significantly outperforms prior methods in both accuracy and interpretability.
  arXiv  Detail & Related papers  (2026-02-26T19:17:39Z)
• SciRAG: Adaptive, Citation-Aware, and Outline-Guided Retrieval and Synthesis for Scientific Literature [52.36039386997026]
  We introduce SciRAG, an open-source framework for scientific literature exploration.<n>We introduce three key innovations: (1) adaptive retrieval that flexibly alternates between sequential and parallel evidence gathering; (2) citation-aware symbolic reasoning that leverages citation graphs to organize and filter documents; and (3) outline-guided synthesis that plans, critiques, and refines answers to ensure coherence and transparent attribution.
  arXiv  Detail & Related papers  (2025-11-18T11:09:19Z)
• Review of Hallucination Understanding in Large Language and Vision Models [65.29139004945712]
  We present a framework for characterizing both image and text hallucinations across diverse applications.<n>Our investigations reveal that hallucinations often stem from predictable patterns in data distributions and inherited biases.<n>This survey provides a foundation for developing more robust and effective solutions to hallucinations in real-world generative AI systems.
  arXiv  Detail & Related papers  (2025-09-26T09:23:08Z)
• Bridging the Plausibility-Validity Gap by Fine-Tuning a Reasoning-Enhanced LLM for Chemical Synthesis and Discovery [0.0]
  Large Language Models frequently generate outputs that appear scientifically reasonable yet violate fundamental principles.<n>We present a systematic approach combining a reasoning-centric model architecture with Low-Rank Adaptation fine-tuning.<n> Evaluation reveals substantial improvements: the fine-tuned system achieves 96.3% format adherence, 97.4% chemical validity, and 74.4% synthesis feasibility.
  arXiv  Detail & Related papers  (2025-07-09T23:05:23Z)
• Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation [78.78421340836915]
  We systematically investigate reference-free hallucination detection in open-domain long-form responses.<n>Our findings reveal that internal states are insufficient for reliably distinguishing between factual and hallucinated content.<n>We introduce a new paradigm, named RATE-FT, that augments fine-tuning with an auxiliary task for the model to jointly learn with the main task of hallucination detection.
  arXiv  Detail & Related papers  (2025-05-18T07:10:03Z)
• MedHal: An Evaluation Dataset for Medical Hallucination Detection [4.98142540436183]
  We present MedHal, a novel large-scale dataset specifically designed to evaluate if models can detect hallucinations in medical texts.<n>MedHal addresses gaps by: (1) incorporating diverse medical text sources and tasks; (2) providing a substantial volume of annotated samples suitable for training medical hallucination detection models; and (3) including explanations for factual inconsistencies to guide model learning.
  arXiv  Detail & Related papers  (2025-04-11T14:55:15Z)
• Can Large Language Models Help Experimental Design for Causal Discovery? [94.66802142727883]
  Large Language Model Guided Intervention Targeting (LeGIT) is a robust framework that effectively incorporates LLMs to augment existing numerical approaches for the intervention targeting in causal discovery.<n>LeGIT demonstrates significant improvements and robustness over existing methods and even surpasses humans.
  arXiv  Detail & Related papers  (2025-03-03T03:43:05Z)
• HuDEx: Integrating Hallucination Detection and Explainability for Enhancing the Reliability of LLM responses [0.12499537119440242]
  This paper proposes an explanation enhanced hallucination-detection model, coined as HuDEx.<n>The proposed model provides a novel approach to integrate detection with explanations, and enable both users and the LLM itself to understand and reduce errors.
  arXiv  Detail & Related papers  (2025-02-12T04:17:02Z)
• Smoke and Mirrors in Causal Downstream Tasks [59.90654397037007]
  This paper looks at the causal inference task of treatment effect estimation, where the outcome of interest is recorded in high-dimensional observations.<n>We compare 6 480 models fine-tuned from state-of-the-art visual backbones, and find that the sampling and modeling choices significantly affect the accuracy of the causal estimate.<n>Our results suggest that future benchmarks should carefully consider real downstream scientific questions, especially causal ones.
  arXiv  Detail & Related papers  (2024-05-27T13:26:34Z)
• Fine-grained Hallucination Detection and Editing for Language Models [109.56911670376932]
  Large language models (LMs) are prone to generate factual errors, which are often called hallucinations. We introduce a comprehensive taxonomy of hallucinations and argue that hallucinations manifest in diverse forms. We propose a novel task of automatic fine-grained hallucination detection and construct a new evaluation benchmark, FavaBench.
  arXiv  Detail & Related papers  (2024-01-12T19:02:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.