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001 on1523697376;

003 OCoLC;

005 20250616114728.0;

006 m o d ;

007 cr unu||||||||;

008 250616s2025 ncu obm 000 0 eng d;

040 a| EREb| enge| rdac| ERE;

035 a| (OCoLC)1523697376;

049 a| EREE;

100 1 a| Anthamola, Harishwar Reddye| author?| UNAUTHORIZED;

245 10 a| METAMORPHIC TESTING FOR FAIRNESS EVALUATION IN LARGE LANGUAGE MODELS;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2025.;

300 a| 73 pages;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| application/pdfc| 1.38 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 c| East Carolina Universityd| 2025.;

504 a| Includes bibliographical references.;

520 3 a| Large 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.;

653 a| Large Language Models, Fairness, Metamorphic Testing, Bias Detection, GPT, LLaMA, Natural Language Processing, AI Ethics;

700 1 a| Madhusudan, Dr.Srinivasane| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University=| ^A637090;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/14051;

949 a| Click on web addressw| ASISh| JOYNER101;

949 a| Click on web addressw| ASISh| HSL111;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 6584137;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6584137-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 6/16/2025x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6584137-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 6/16/2025x| ETDz| HERESOURCE;

METAMORPHIC TESTING FOR FAIRNESS EVALUATION IN LARGE LANGUAGE MODELS

Author/creator	Anthamola, Harishwar Reddy author
Other author	Madhusudan, Dr.Srinivasan degree supervisor.
Other author	East Carolina University
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2025.
Description	73 pages
Supplemental Content	Access via ScholarShip

Summary	Large 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.
Dissertation note	East Carolina University 2025.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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METAMORPHIC TESTING FOR FAIRNESS EVALUATION IN LARGE LANGUAGE MODELS

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