LLM Information Extraction

LLM: Large Language Model

This field studies how to turn text, tables, images, and long documents into structured data that can support search, analysis, and decision-making. Common tasks include named-entity and relation extraction, event and attribute extraction, clinical report parsing, and building knowledge graphs from scientific or legal literature. Approaches range from prompting and retrieval-augmented examples to fine-tuning, schema-constrained decoding, agent workflows, and verification against source evidence. Models can produce well-formed outputs, but accurate and complete values remain difficult in long, noisy, multimodal, or specialized documents, so evaluation, provenance, and human review remain central.

Latest papers

238 papers31 papers in the last four weeks, down 3% on the four weeks before. 0.2% of all new papers.

MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning
Apr 18, 2026Ruijun Huang, Zhiqiao Kang, Yuxuan Zhu +5LLM Hallucination Mitigation Document Information Extraction
1Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology
CBRS: Cognitive Blood Request System with Bilingual Dataset and Dual-Layer Filtering for Multi-Platform Social Streams
Apr 17, 2026Anik Saha, Mst. Fahmida Sultana Naznin, Zia Ul Hassan Abdullah +3LLM Information Extraction
Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
DiZiNER: Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognition
Apr 17, 2026Siun Kim, Hyung-Jin YoonNamed Entity Recognition LLM-Assisted Annotation
Seltasquare Seoul, Korea · Biomedical Research Institute, Seoul National University Hospital · Seoul National University College of Medicine Seoul, Korea
IE as Cache: Information Extraction Enhanced Agentic Reasoning
Apr 16, 2026Hang Lv, Sheng Liang, Hongchao Gu +5Agentic Reasoning LLM Information Extraction
University of Science and Technology of China · Huawei Technologies Co., Ltd.
Using reasoning LLMs to extract SDOH events from clinical notes
Apr 15, 2026Ertan Dogan, Kunyu Yu, Yifan PengClinical Information Extraction LLM Information Extraction
Department of Population Health Sciences, Weill Cornell Medicine, New York, USA
ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction
Apr 12, 2026Wenda Liu, Zhigang Song, Shuai Nie +11LLM Information Extraction Curriculum Learning
Xiaomi Corporation, Beijing, China · Xi’an Jiaotong-Liverpool University, Suzhou, China
ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery
Apr 10, 2026Shahar Levy, Eliya Habba, Reshef Mintz +3LLM-Assisted Annotation Document Information Extraction
School of Computer Science and Engineering, The Hebrew University of Jerusalem · Faculty of Law, The Hebrew University of Jerusalem · Allen Institute for AI
LLMs Underperform Graph-Based Parsers on Supervised Relation Extraction for Complex Graphs
Apr 9, 2026Paolo Gajo, Domenic Rosati, Hassan Sajjad +1Relation Extraction LLM Information Extraction
University of Bologna · Dalhousie University
From Papers to Property Tables: A Priority-Based LLM Workflow for Materials Data Extraction
Apr 8, 2026Koushik Rameshbabu, Jing Luo, Ali Shargh +2Document Information Extraction LLM Information Extraction
Department of Applied Mathematics and Statistics, Johns Hopkins University · Department of Mechanical Engineering, Johns Hopkins University
Curation and Extraction of Drug-Related Entities from Reddit Platform
Apr 8, 2026Zewei Wang, Zihan Xu, Yishu Wei +2Named Entity Recognition Clinical Information Extraction
Population Health Sciences, Weill Cornell Medicine, New York City, USA · School of Computing and Information Systems, University of Melbourne, Melbourne, Australia · Emergency Medicine, Weill Cornell Medicine, New York City, USA
LLM-based Schema-Guided Extraction and Validation of Missing-Person Intelligence from Heterogeneous Data Sources
Apr 8, 2026Joshua Castillo, Ravi MukkamalaDocument Information Extraction LLM Information Extraction
Department of Computer Science Old Dominion University Norfolk, Virginia, USA
FMI@SU ToxHabits: Evaluating LLMs Performance on Toxic Habit Extraction in Spanish Clinical Texts
Apr 7, 2026Sylvia Vassileva, Ivan Koychev, Svetla BoytchevaClinical Information Extraction Named Entity Recognition
Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, Sofia, Bulgaria · Graphwise, Sofia, Bulgaria
FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosures
Apr 7, 2026Fan Zhang, Mingzi Song, Rania Elbadry +20Financial Services LLM Information Extraction
Steve · MBZUAI · The University of Tokyo +8
STIndex: A Context-Aware Multi-Dimensional Spatiotemporal Information Extraction System
Apr 7, 2026Wenxiao Zhang, Yu Liu, Qiang sun +5Document Information Extraction LLM Information Extraction
The University of Western Australia Perth, Australia · Institute of Information Engineering, Chinese Academy of Sciences University of Chinese Academy of Sciences, Beijing, China · Murdoch University Perth, Australia
Instruction-Tuned LLMs for Parsing and Mining Unstructured Logs on Leadership HPC Systems
Apr 6, 2026Ahmad Maroof Karimi, Jong Youl Choi, Charles Qing Cao +1Document Parsing LLM Information Extraction
Oak Ridge National Laboratory, Oak Ridge, USA · University of Tennessee, Knoxville, USA
Method for Aggregating Unstructured Data Using Large Language Models
Apr 4, 2026Vsevolod Lazebnyi, Natalia Tereshkina, Maria Shabarina +1LLM Information Extraction
ITMO University, Saint-Petersburg, Russia
De Jure: Iterative LLM Self-Refinement for Structured Extraction of Regulatory Rules
Apr 2, 2026Keerat Guliani, Deepkamal Gill, David Landsman +3Document Information Extraction Legal Document Analysis
The Vanguard Group, Inc.
A Role-Based LLM Framework for Structured Information Extraction from Healthy Food Policies
Apr 2, 2026Congjing Zhang, Ruoxuan Bao, Jingyu Li +3Document Information Extraction LLM Information Extraction
Department of Industrial & Systems Engineering, University of Washington, Seattle, WA, USA · Department of Management, Shanghai University, Shanghai, China · H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA +1
Reducing Hallucinations in LLM-based Scientific Literature Analysis Using Peer Context Outlier Detection
Apr 1, 2026Daniel Xie, Maxwell J. Jacobson, Adil Wazeer +3LLM Hallucination Mitigation LLM Information Extraction
Department of Computer Science, Purdue University, IN, USA · School of Materials Engineering, Purdue University, IN, USA
A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization
Apr 1, 2026Maxwell J. Jacobson, Daniel Xie, Jackson Shen +6Multi-Agent LLM Systems AI-Assisted Scientific Research
Department of Computer Science, Purdue University, IN, USA · School of Materials Science and Engineering, Purdue University, IN, USA
Information Extraction from Electricity Invoices with General-Purpose Large Language Models
Apr 1, 2026Javier Gómez, Javier SánchezDocument Information Extraction LLM Information Extraction
Centro de Tecnologías de la Imagen (CTIM) Instituto Universitario de Cibernética, Empresas y Sociedad (IUCES) Universidad de Las Palmas de Gran Canaria 35017 Las Palmas de Gran Canaria, Spain · Centro de Tecnologías de la Imagen (CTIM) Instituto Universitario de Cibernética, Empresas y Sociedad (IUCES) University of Las Palmas de Gran Canaria 35017 Las Palmas de Gran Canaria, Spain
Webscraper: Leverage Multimodal Large Language Models for Index-Content Web Scraping
Mar 31, 2026Guan-Lun Huang, Yuh-Jzer JoungWeb Agents LLM Information Extraction
Dept. of Information Management, National Taiwan University, Taipei, Taiwan
Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats
Mar 27, 2026Pierre Epron, Adrien Coulet, Mehwish AlamNamed Entity Recognition LLM Information Extraction
HeKA | U1346, DIG · Inria, Inserm, Université Paris Cité, HeKA, UMR 1346, Paris, France · Télécom Paris, Institut Polytechnique de Paris, France +2
Semi-Automated Knowledge Engineering and Process Mapping for Total Airport Management
Mar 27, 2026Darryl Teo, Adharsha Sam, Chuan Shen Marcus Koh +2KG Construction LLM Information Extraction
Aviation Studies Institute, Singapore University of Technology and Design, Singapore
SeaAlert: Robust Severity Classification and LLM-Based Information Extraction for Noisy Maritime Distress Communications
Mar 23, 2026Tomer Atia, Yehudit Aperstein, Alexander ApartsinLLM Information Extraction Language Model Robustness
School of Computer Science, Faculty of Sciences, HIT-Holon Institute of Technology, Holon 58102, Israel · Intelligent Systems, Afeka Academic College of Engineering, Tel Aviv 69988, Israel
Context Selection for Hypothesis and Statistical Evidence Extraction from Full-Text Scientific Articles
Mar 22, 2026Sai Koneru, Jian Wu, Sarah RajtmajerDocument Information Extraction LLM Information Extraction
Pennsylvania State University, University Park, Pennsylvania, USA · Old Dominion University, Norfolk, Virginia, USA
Can Large Language Models Reliably Extract Physiology Index Values from Coronary Angiography Reports?
Mar 21, 2026Sofia Morgado, Filipa Valdeira, Niklas Sander +4Clinical Information Extraction LLM Information Extraction
Sofia Morgado is with the Computer Science Department at Nova School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal (FCT-UNL) · Filipa Valdeira is with Center for Computational and Stochastic Mathematics - Department of Mathematics at Instituto Superior Técnico · Niklas Sander is with the Department of Computer Science at FCT-UNL +4
Evaluating AI-based Scientific Knowledge Synthesis with Epidemiological Systematic Reviews
Mar 20, 2026Shreyansh Padarha, Ryan Othniel Kearns, Tristan Naidoo +13Systematic Review Automation LLM Evaluation
University of Oxford · Imperial College London · University of Nottingham +2
SlovKE: A Large-Scale Dataset and LLM Evaluation for Slovak Keyphrase Extraction
Mar 16, 2026David Števaňák, Marek ŠuppaDocument Information Extraction Low-Resource Language Processing
University of Vienna, Austria · NaiveNeuron · Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava +1
Extracting Breast Cancer Phenotypes from Clinical Notes: Comparing LLMs with Classical Ontology Methods
Mar 16, 2026Abdullah Bin Faiz, Arbaz Khan Shehzad, Asad Afzal +5Clinical Information Extraction LLM Information Extraction
CureMD Research, 80 Pine Street, New York, 10005, USA · Oncology Department, Shifa International Hospital, 4 Pitras Bukhari Rd, H-8/4 H 8/4 H-8, Islamabad, Pakistan

Apr 18, 2026J/K move · Enter open · S save

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

Ruijun Huang, Zhiqiao Kang, Yuxuan Zhu, Junxiong Li+4

1Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology

LLM Hallucination Mitigation Document Information Extraction LLM Information Extraction

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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.