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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp014j03d2990
Title: Inclusive Intelligence: Advancing Fairness in Large Language Models through Fine-Tuning and Hyperparameter Optimization
Authors: Gupta, Kanak
Advisors: Hanin, Boris
Department: Operations Research and Financial Engineering
Certificate Program: Applications of Computing Program
Class Year: 2024
Abstract: This thesis explores how fine-tuning the bert-base-uncased model can reduce gender and racial-gender intersectional biases and improve fairness performance on WEAT (word embedding association test) and SEAT (sentence embedding association test) metrics. We also study how the QLoRA fine-tuning method’s hyperparameters α and r impact the fairness performance of fine-tuned models. We find that increasing r decreases overall bias, however, increasing α initially decreases overall bias and then increases it. The dataset used for fine-tuning is a novel dataset we created composed of academic scholarship on gender. Our fine-tuning efforts with this dataset were successful as some of our fine-tuned models outperformed the BERT base model on the embedding distance-based fairness evaluation tests we used. Several biases related to occupation and (occupation-related) skills embeddings were studied. Gender groups like female, male, and gender non-binary and subgroups like white female, black female, white male, and black male were considered in the scope of this work.
URI: http://arks.princeton.edu/ark:/88435/dsp014j03d2990
Type of Material: Princeton University Senior Theses
Language: en
Appears in Collections:Operations Research and Financial Engineering, 2000-2024

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