Zero-Shot Statistical Tests for LLM-Generated Text Detection using Finite Sample Concentration Inequalities

• URL: http://arxiv.org/abs/2501.02406v3
• Date: Sat, 12 Apr 2025 18:05:42 GMT
• Title: Zero-Shot Statistical Tests for LLM-Generated Text Detection using Finite Sample Concentration Inequalities
• Authors: Tara Radvand, Mojtaba Abdolmaleki, Mohamed Mostagir, Ambuj Tewari,
• Abstract summary: Verifying the provenance of content is crucial to the function of many organizations, e.g., educational institutions, social media platforms, firms, etc.<n>This problem is becoming increasingly challenging as text generated by Large Language Models (LLMs) becomes almost indistinguishable from human-generated content.<n>We show that our tests' type I and type II errors decrease exponentially as text length increases.<n> Practically, our work enables guaranteed finding of the origin of harmful or false LLM-generated text, which can be useful for combating misinformation and compliance with emerging AI regulations.
• Score: 13.657259851747126
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 challenging 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. 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 our tests' type I and type II errors decrease exponentially as text length increases. For designing our tests for a given string, we demonstrate that if the string is generated by the evaluator model $A$, the log-perplexity of the string under $A$ converges to the average entropy of the string under $A$, except with an exponentially small probability in the string length. We also show that if $B$ generates the text, except with an exponentially small probability in string length, the log-perplexity of the string under $A$ converges to the average cross-entropy of $B$ and $A$. For our experiments: First, we present experiments using open-source LLMs to support our theoretical results, and then we provide experiments in a black-box setting with adversarial attacks. Practically, our work enables guaranteed finding of the origin of harmful or false LLM-generated text, which can be useful for combating misinformation and compliance with emerging AI regulations.

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