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.

LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes
Mar 14, 2026Mingchen Shao, Yuzhang Xie, Carl Yang +1Alzheimer's Disease Clinical Information Extraction
Northwestern University, Evanston, IL · Emory University, Atlanta, GA
Benchmarking Large Language Models on Reference Extraction and Parsing in the Social Sciences and Humanities
Mar 13, 2026Yurui Zhu, Giovanni Colavizza, Matteo RomanelloDocument Information Extraction LLM Information Extraction
Odoma LLC
Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SCILIRE System
Mar 13, 2026Necva Bölücü, Jessica Irons, Changhyun Lee +5LLM Information Extraction Scientific Workflow Automation
CSIRO, Sydney, Australia · ITIS, University of Málaga, Málaga, Spain
DocSage: An Information Structuring Agent for Multi-Doc Multi-Entity Question Answering
Mar 12, 2026Teng Lin, Yizhang Zhu, Zhengxuan Zhang +2Multi-Document QA Multi-Hop QA
The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology
Performance Evaluation of Open-Source Large Language Models for Assisting Pathology Report Writing in Japanese
Mar 12, 2026Masataka Kawai, Singo Sakashita, Shumpei Ishikawa +8Medical Report Generation Medical LLMs
Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan · Department of Pathology, University of Yamanashi, Chuo, Japan · Division of Pathology, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Japan +3
Tracking Cancer Through Text: Longitudinal Extraction From Radiology Reports Using Open-Source Large Language Models
Mar 10, 2026Luc Builtjes, Alessa HeringClinical Information Extraction LLM Information Extraction
Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands
A Consensus-Driven Multi-LLM Pipeline for Missing-Person Investigations
Mar 9, 2026Joshua Castillo, Ravi MukkamalaMulti-LLM Collaboration LLM Information Extraction
Old Dominion University, Norfolk VA 23529, USA
Tabular PDF Information Extraction with Local LLMs and Layout-Aware Parsing: A Reliability Evaluation
Mar 9, 2026Muhammad Anis Al Hilmi, Neelansh Khare, Noel Framil Iglesias +3Document Information Extraction Document Parsing
Informatics, Faculty of Engineering, Universitas Swadaya Gunung Jati, Cirebon, Indonesia · Computer Science, University of California, Irvine, USA · Computer Engineering, UNIR, La Rioja, Spain +1
Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction
Mar 7, 2026Özgür Togay, Javier Garcia-Bernardo, Florian Kunneman +1Stance Detection Political Discourse Analysis
Department of Methodology and Statistics, Utrecht University · Department of Languages, Literature and Communication, Utrecht University
A Dynamic Self-Evolving Extraction System
Mar 6, 2026Moin Amin-Naseri, Hannah Kim, Estevam HruschkaDocument Information Extraction LLM Information Extraction
Megagon Labs
OpenExtract: Automated Data Extraction for Systematic Reviews in Health
Mar 6, 2026Jim Achterberg, Bram Van Dijk, Jing Meng +8Systematic Review Automation Document Information Extraction
Leiden University Medical Center, The Netherlands · Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands · University of Warwick, UK +2
T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning
Mar 4, 2026Qinsi Wang, Hancheng Ye, Jinhee Kim +12Structured Reasoning LLM Reasoning
Duke University · UT Austin · Meta
LexChronos: An Agentic Framework for Structured Event Timeline Extraction in Indian Jurisprudence
Mar 2, 2026Anka Chandrahas Tummepalli, Preethu Rose AnishLegal NLP Document Information Extraction
TCS Research
A Human-Centered Workflow for Using Large Language Models in Content Analysis
Feb 27, 2026Ivan ZupicLLM-Assisted Annotation Human-in-the-Loop Annotation
School of Creative Management Goldsmiths, University of London
Real-Time Trustworthiness Scoring for LLM Structured Outputs and Data Extraction
Feb 24, 2026Hui Wen Goh, Jonas MuellerLLM Answer Verification LLM Uncertainty Estimation
Cleanlab
Agentic Framework for Political Biography Extraction
Feb 23, 2026Yifei Zhu, Songpo Yang, Jiangnan Zhu +1Document Information Extraction LLM Information Extraction
Ph.D candidate, Department of Politics and Public Administration, The University of Hong Kong. · Boya Postdoctoral Fellow, School of International Studies, Peking University. · Associate Professor, Department of Politics and Public Administration, The University of Hong Kong. +1
AIDG: A Formal Decomposition of Information Extraction and Containment Asymmetries in Multi-Turn LLM Dialogue
Feb 19, 2026Adib Sakhawat, Fardeen Sadab, Rakin ShahriarPartially Observable Stochastic Games Multi-Turn Dialogue Evaluation
Department of Computer Science and Engineering, Islamic University of Technology, Dhaka, Bangladesh
Extracting Consumer Insight from Text: A Large Language Model Approach to Emotion and Evaluation Measurement
Feb 17, 2026Stephan Ludwig, Peter J. Danaher, Xiaohao Yang +4Sentiment Analysis LLM Information Extraction
Monash University, Australia · University of New South Wales, Australia · Flinders University, Australia +3
ScrapeGraphAI-100k: Dataset for Schema-Constrained LLM Generation
Feb 16, 2026William Brach, Francesco Zuppichini, Marco Vinciguerra +1Schema-Constrained Generation LLM Information Extraction
ScrapeGraphAI, San Francisco, United States
LLMStructBench: Benchmarking Large Language Model Structured Data Extraction
Feb 16, 2026Sönke Tenckhoff, Mario Koddenbrock, Erik RodnerLLM Information Extraction Document Parsing Benchmarks
KI-Werkstatt / FB2, University of Applied Sciences Berlin
Do not be greedy, Think Twice: Sampling and Selection for Document-level Information Extraction
Jan 26, 2026Mikel Zubillaga, Oscar Sainz, Oier Lopez de Lacalle +1Self-Consistency Decoding Document Information Extraction
HiTZ Center - Ixa, University of the Basque Country UPV/EHU
Assessment of Generative Named Entity Recognition in the Era of Large Language Models
Jan 25, 2026Qi Zhan, Yile Wang, Hui HuangNamed Entity Recognition LLM Information Extraction
College of Computer Science and Software Engineering, Shenzhen University
Strategies for Span Labeling with Large Language Models
Jan 23, 2026Danil Semin, Ondřej Dušek, Zdeněk KasnerConstrained Decoding Language Model Decoding
Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics, Charles University
Rep2Text: Decoding Full Text from a Single LLM Token Representation
Nov 9, 2025Haiyan Zhao, Zirui He, Yiming Tang +4LLM Interpretability LLM Information Extraction
New Jersey Institute of Technology · National University of Singapore · Wake Forest University +2
LeMat-Synth: a multi-modal toolbox to curate broad synthesis procedure databases from scientific literature
Oct 28, 2025Magdalena Lederbauer, Siddharth Betala, Valerie Gentzke +22Document Information Extraction Materials Science
Entalpic · ENSA Lyon UMR-MAP Aria · Independent Researcher +8
Auditing a KB Elicitation of Frontier LLM Knowledge: A Multi-dimensional Analysis of GPTKB v1.5
Oct 8, 2025Shrestha Ghosh, Luca Giordano, Yujia Hu +2Factual Knowledge in Language Models LLM Auditing
University of Tübingen · ScaDS.AI & TU Dresden · VNU University of Engineering and Technology
GPTKB v1.5: A Massive Knowledge Base for Exploring Factual LLM Knowledge
Jul 8, 2025Yujia Hu, Tuan-Phong Nguyen, Shrestha Ghosh +2KG Construction LLM Information Extraction
ScaDS.AI Dresden/Leipzig & TU Dresden, Germany · VNU University of Engineering and Technology, Hanoi, Vietnam · University of Tübingen, Germany
Towards Large Language Models for Lunar Mission Planning and In Situ Resource Utilization
Apr 28, 2025Michael Pekala, Gregory Canal, Samuel Barham +5LLM Information Extraction
Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road, Laurel, Maryland 20723, USA

Mar 14, 2026J/K move · Enter open · S save

LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes

Mingchen Shao, Yuzhang Xie, Carl Yang, Jiaying Lu

Northwestern University, Evanston, IL · Emory University, Atlanta, GA

Alzheimer's Disease Clinical Information Extraction LLM Information Extraction

Open paper AskPDF arXiv

Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage detection and disease staging. However, this information is usually embedded in unstructured textual data rather than tabular data, making it difficult to be extracted accurately. We therefore propose LLM-MINE, a Large Language Model-based phenotype mining framework for automatic extraction of ADRD phenotypes from clinical notes. Using two expert-defined phenotype lists, we evaluate the extracted phenotypes by examining their statistical significance across cohorts and their utility for unsupervised disease staging. Chi-square analyses confirm statistically significant phenotype differences across cohorts, with memory impairment being the strongest discriminator. Few-shot prompting with the combined phenotype lists achieves the best clustering performance (ARI=0.290, NMI=0.232), substantially outperforming biomedical NER and dictionary-based baselines. Our results demonstrate that LLM-based phenotype extraction is a promising tool for discovering clinically meaningful ADRD signals from unstructured notes.