Tutorial: Extracting Unstructured Text Using Large Language Models

This tutorial shows how to build a two-stage pipeline in which a multimodal large language model reads each page as an image and a second model turns the extracted text into structured JSON. We cover the choices that make such pipelines reliable and affordable, including structured outputs, parameter control, task decomposition, cost-effective model selection, and validation against a human-verified ground truth.

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Publication Details

Journal
INFORMS Journal on Applied Analytics
Published
2026-10-09
DOI
https://doi.org/10.1287/inte.2026.0314
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
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article

Tutorial: Extracting Unstructured Text Using Large Language Models

Oliver Schaer, Simon Spavound, Panos Markou
INFORMS Journal on Applied Analytics
Topic Modeling
article

Tutorial: Extracting Unstructured Text Using Large Language Models

Oliver Schaer, Simon Spavound, Panos Markou
article en

Abstract

This tutorial shows how to build a two-stage pipeline in which a multimodal large language model reads each page as an image and a second model turns the extracted text into structured JSON. We cover the choices that make such pipelines reliable and affordable, including structured outputs, parameter control, task decomposition, cost-effective model selection, and validation against a human-verified ground truth.

INFORMS Journal on Applied Analytics
University of Virginia (US), Drexel University (US)
Openalex Percentile: Top 12%
Topic Modeling
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