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Zero-Shot Statistical Tests for LLM-Generated Text Detection using Finite Sample Concentration Inequalities by Radvand, Tara; Abdolmaleki, Mojtaba; Mostagir, Mohamed; Tewari, Ambuj is a scholarly article available to read on EtoBox.

What is Zero-Shot Statistical Tests for LLM-Generated Text Detection using Finite Sample Concentration Inequalities about?

Verifying the provenance of content is crucial to the function of many organizations, e.g., educational institutions, social media platforms, firms, etc. This problem is becoming increasingly difficult as text generated by Large Language Models (LLMs) becomes almost indistinguishable from human-generated content. In addition, many institutions utilize in-house LLMs and want to ensure that external, non-sanctioned LLMs do not produce content within the institution. In this paper, we answer the following question: Given a piece of text, can we identify whether it was produced by LLM $A$ or $B$ (where $B$ can be a human)? We model LLM-generated text as a sequential stochastic process with complete dependence on history and design zero-shot statistical tests to distinguish between (i) the text generated by two different sets of LLMs $A$ (in-house) and $B$ (non-sanctioned) and also (ii) LLM-generated and human-generated texts. We prove that the type I and type II errors for our tests decrease exponentially in the text length. In designing our tests, we derive concentration inequalities on the difference between log-perplexity and the average entropy of the string under $A$. Specifically

Author

Radvand, Tara; Abdolmaleki, Mojtaba; Mostagir, Mohamed; Tewari, Ambuj

Published

2025

Language

EN