A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts

Anonymous

TL;DR: We investigate whether paraphrasing with large language models should alter text authorship attribution through empirical analysis and a philosophical perspective.

Abstract: In the realm of text manipulation and linguistic transformation, the question of authorship has always been a subject of fascination and philosophical inquiry. Much like the $\textbf{Ship of Theseus paradox}$, which ponders whether a ship remains the same when each of its original planks is replaced, our research delves into an intriguing question: $\textit{Does a text retain its original authorship when it undergoes numerous paraphrasing iterations?}$ Specifically, since Large Language Models (LLMs) have demonstrated remarkable proficiency in both the generation of original content and the modification of human-authored texts, a pivotal question emerges concerning the determination of authorship in instances where LLMs or similar paraphrasing tools are employed to rephrase the text - $\textit{whether authorship should be attributed to the original human author or the AI-powered tool.}$ Therefore, we embark on a philosophical voyage through the seas of language and authorship to unravel this intricate puzzle. Using a computational approach, we discover that the diminishing performance in text classification models with each successive paraphrasing iteration is closely associated with the extent of deviation from the original author's style, thus provoking a reconsideration of the current notion of authorship.

Paper Type: long

Research Area: Interpretability and Analysis of Models for NLP

Contribution Types: Data analysis, Position papers

Languages Studied: English