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

• URL: http://arxiv.org/abs/2501.02406v4
• Date: Fri, 16 May 2025 15:45:11 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 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>In this paper, we answer the following question: Given a piece of text, can we identify whether it was produced by a particular LLM or not?<n>We model LLM-generated text as a sequential process with complete dependence on history. We then design zero-shot statistical tests to distinguish between text generated by two different known sets of LLM
• 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. In this paper, we answer the following question: Given a piece of text, can we identify whether it was produced by a particular LLM or not? We model LLM-generated text as a sequential stochastic process with complete dependence on history. We then design zero-shot statistical tests to (i) distinguish between text generated by two different known sets of LLMs $A$ (non-sanctioned) and $B$ (in-house), and (ii) identify whether text was generated by a known LLM or generated by any unknown model, e.g., a human or some other language generation process. We prove that the type I and type II errors of our test decrease exponentially with the length of the text. For that, we show that if $B$ generates the text, then 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 $A$. We then present experiments using LLMs with white-box access to support our theoretical results and empirically examine the robustness of our results to black-box settings and adversarial attacks. In the black-box setting, our method achieves an average TPR of 82.5\% at a fixed FPR of 5\%. Under adversarial perturbations, our minimum TPR is 48.6\% at the same FPR threshold. Both results outperform all non-commercial baselines. See https://github.com/TaraRadvand74/llm-text-detection for code, data, and an online demo of the project.

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