Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Thomas Austin
Third Advisor
Genya Ishigaki
Keywords
Sequence-to-Sequence Models, Style Transfer, Natural Language Processing (NLP), Large Language Models (LLMs), Adversarial Text Transformation
Abstract
With the advancements in Large Language Models (LLMs) such as ChatGPT, the boundary between text written by a human and that created by AI has become blurred. This poses a threat to systems designed to detect AI-generated content. In this work, we adopt a novel strategy to adversarially transform GPT-generated text using sequence-to-sequence (Seq2Seq) models, to make the text more human-like. We concentrate on improving GPT-generated sentences by including significant linguistic, structural, and semantic components that are typical of human-authored text. The goal is to then use this transformed data to train a more robust detection model. Our experiments show that our Seq2Seq approach makes it difficult for classifiers to detect AI-generated text. On the other hand, once retrained on this augmented data, models become more robust with improved accuracy in distinguishing AI-generated from human-generated content. This work adds to the ongoing conversation of text transformation as a tool for both attack (in the sense of defeating classification models) and defense (in the sense of improved classifiers), thereby advancing our understanding of AI-generated text detection.
Recommended Citation
Reddy, Natesh, "Transforming Chatbot Text: A Sequence-to-Sequence Approach to Human-Like Text Generation" (2025). Master's Projects. 1560.
DOI: https://doi.org/10.31979/etd.ya8w-kmhj
https://scholarworks.sjsu.edu/etd_projects/1560