Work

Assessing the Reliability of LLMs Annotations in the Context of Demographic Bias and Model Explanation

Year: 2025

Type: conference-paper

Abstract: The paper contains examples which are offensive in nature.

Source: Workshop on Gender Bias in Natural Language Processing (GeBNLP)

DOI: 10.18653/v1/2025.gebnlp-1.9

Authors: Hadi Mohammadi, Tina Shahedi, Pablo Mosteiro, Massimo Poesio, Ayoub Bagheri, Anastasia Giachanou

Institutions: Utrecht University, Queen Mary University of London

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: hybrid

Funders: Universiteit Utrecht, Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Award: Dealing with meaning variation in NLP

OpenAlex ID: W4412944405

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