Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration

2026-08-06

AI Summary

This work addresses the limitations of existing zero-shot detectors, which often fail to robustly identify machine-generated text due to their neglect of the generative mechanisms inherent in large language models. To overcome this, we propose EchoPrompt, a training-free detection method that introduces implicit prompt recovery into the zero-shot detection framework for the first time. EchoPrompt activates latent dependencies embedded in generated text through a unified prefix and quantifies a text’s reliance on its underlying prompt by measuring the likelihood gain discrepancy between an instruction-tuned model and its base counterpart. Extensive experiments demonstrate that EchoPrompt achieves state-of-the-art performance across multiple challenging scenarios while maintaining strong robustness, significantly outperforming current zero-shot detectors.

Abstract

Large language models (LLMs) can generate fluent and convincing text at scale, creating growing risks for misinformation dissemination, educational misuse, and platform governance. These concerns make robust detection of machine-generated text increasingly necessary. Recent zero-shot detectors mainly exploit probability-based statistical discrepancies, but they do not explicitly account for the training process of LLMs, which leaves a distinct generation mechanism insufficiently modeled and limits detection robustness. To address this issue, we propose EchoPrompt, a training-free detector based on latent prompt restoration. Our key intuition is that machine-generated text is typically produced conditioned on an upstream prompt, and this hidden dependency can be partially reactivated by prepending a unified generic prefix. Specifically, EchoPrompt restores a generic assistant-response context, measures the induced likelihood gain with an instruction-tuned model, calibrates it against the corresponding base model, and aggregates the resulting differences into a score that quantifies latent prompt dependency. Extensive experiments show that EchoPrompt achieves state-of-the-art performance among zero-shot detectors while maintaining strong robustness across challenging evaluation settings.

Problem

• LLM-generated text detection
• zero-shot detection
• latent prompt dependency
• robustness
• machine-generated text

Innovation

• latent prompt restoration
• zero-shot detection
• LLM-generated text detection
• EchoPrompt
• likelihood calibration