Nationality Bias in Text Generation

Pranav Narayanan Venkit, Sanjana Gautam, Ruchi Panchanadikar, Ting Hao Huang, Shomir Wilson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Little attention is placed on analyzing nationality bias in language models, especially when nationality is highly used as a factor in increasing the performance of social NLP models. This paper examines how a text generation model, GPT-2, accentuates pre-existing societal biases about country-based demonyms. We generate stories using GPT-2 for various nationalities and use sensitivity analysis to explore how the number of internet users and the country's economic status impacts the sentiment of the stories. To reduce the propagation of biases through large language models (LLM), we explore the debiasing method of adversarial triggering. Our results show that GPT-2 demonstrates significant bias against countries with lower internet users, and adversarial triggering effectively reduces the same.

Original languageEnglish (US)
Title of host publicationEACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages116-122
Number of pages7
ISBN (Electronic)9781959429449
StatePublished - 2023
Event17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 - Dubrovnik, Croatia
Duration: May 2 2023May 6 2023

Publication series

NameEACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023
Country/TerritoryCroatia
CityDubrovnik
Period5/2/235/6/23

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Software
  • Linguistics and Language

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