Ludwig Maximilians Universit T M Nchen: Research Institution — Prolific Citations Library

Browse 1 peer-reviewed paper from Ludwig Maximilians Universit T M Nchen spanning Annotation Quality, Human-AI Collaboration (2025). Research powered by Prolific's high-quality participant data.

This page lists 1 peer-reviewed paper from researchers at Ludwig Maximilians Universit T M Nchen in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (1 of 1)

Improving annotation quality: empirical insights into bias, human-AI collaboration, and workflow design

Authors: J Beck

Year: 2025

Published in: 2025 - edoc.ub.uni-muenchen.de

Institution: Ludwig-Maximilians-Universität München, University of Bayreuth

Research Area: Annotation Quality, Human-AI Collaboration, Behavioral Science, Human-Computer Interaction (HCI)

Discipline: Human-Computer Interaction (HCI)

The study empirically evaluates annotation bias, proposes strategies to reduce its impact, and explores the use of large language models in automated and hybrid annotation workflows.

Methods: Empirical assessments and experimental evaluations involving annotation workflows and large language models.

Key Findings: Annotation bias, annotation quality, and the effectiveness of hybrid workflows integrating human input and AI models.