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Evidence record 17084 · automatically gathered

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

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 limi

Record details

Published: 6 August 2026
Source: arXiv
Category: Research
Topics: Regulation · Misinformation
Retrieved: 7 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (6 August 2026), “Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration,” evidence record 17084, https://ethics.ai/record/17084 (originally published by arXiv).

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