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METAMORPHIC TESTING FOR FAIRNESS EVALUATION IN LARGE LANGUAGE MODELS

dc.contributor.advisorDr.Srinivasan Madhusudan
dc.contributor.authorAnthamola, Harishwar Reddy
dc.contributor.committeeMemberDr.Tabrizi Nasseh
dc.contributor.committeeMemberDr.Hart David Marvin
dc.contributor.departmentComputer Science
dc.date.accessioned2025-06-05T17:33:01Z
dc.date.available2025-06-05T17:33:01Z
dc.date.created2025-05
dc.date.issuedMay 2025
dc.date.submittedMay 2025
dc.date.updated2025-05-22T21:15:17Z
dc.degree.collegeCollege of Engineering and Technology
dc.degree.grantorEast Carolina University
dc.degree.majorMS-Software Engineering
dc.degree.nameM.S.
dc.degree.programMS-Software Engineering
dc.description.abstractLarge Language Models (LLMs) have made significant progress in Natural Language Processing, yet they remain susceptible to fairness-related issues, often reflecting biases from their training data. These biases present risks, mainly when LLMs are used in sensitive domains such as healthcare, finance, and law. This research proposes a metamorphic testing approach to uncover fairness bugs in LLMs systematically. We define and apply fairness-oriented metamorphic relations (MRs) to evaluate state-of-the-art models like LLaMA and GPT across diverse demographic inputs. By generating and analyzing source and follow-up test cases, we identify patterns of bias, particularly in tone and sentiment. Results show that tone-based MRs detected up to 2,200 fairness violations, while sentiment-based MRs detected fewer than 500, highlighting the strength of this method. This study presents a structured strategy for enhancing fairness in LLMs and improving their robustness in critical applications.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10342/14051
dc.language.isoEnglish
dc.publisherEast Carolina University
dc.subjectComputer Science
dc.titleMETAMORPHIC TESTING FOR FAIRNESS EVALUATION IN LARGE LANGUAGE MODELS
dc.typeMaster's Thesis
dc.type.materialtext

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