Journal article
AnnoLoom: Augmenting Codebook Generation and Annotation with Large Language Models
Proceedings of the Association for Information Science and Technology, v 62(1), pp 1711-1713
Oct 2025
Abstract
ABSTRACT
We introduce AnnoLoom, a tool designed to assist researchers with codebook development, annotation tasks, and evaluation of human vs. AI's annotation results. AnnoLoom contributes to human expert‐AI collaboration and its efficacy in the context of using Large Language Models (LLMs) for research involving text‐based data. We conducted a cognitive walkthrough to iteratively improve the design of AnnoLoom and discussed the future work.
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Details
- Title
- AnnoLoom: Augmenting Codebook Generation and Annotation with Large Language Models
- Creators
- Lu Wang - Stevens Institute of TechnologyDuncan Lynch - Stevens Institute of TechnologyElham Aghakhani - Drexel UniversityGeorge Demiris - California University of PennsylvaniaKarla Washington - Washington University in St. LouisRezvaneh Rezapour - Drexel UniversityJina Huh-Yoo - Stevens Institute of Technology
- Publication Details
- Proceedings of the Association for Information Science and Technology, v 62(1), pp 1711-1713
- Publisher
- Wiley
- Number of pages
- 3
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-105019795303
- Other Identifier
- 991022123314504721