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LLMs & generative AIDetection methods
Can AI-Generated Text be Reliably Detected?This paper stress-tests AI-text detectors using a novel recursive paraphrasing attack, showing that watermarking-based, neural network-based, and retrieval-based detectors can be reliably broken with only minor text degradation. The work establishes a theoretical connection between detector AUROC and the total variation distance between…
LLMs & generative AIDetection methods
On the Risk of Misinformation Pollution with Large Language ModelsThis paper investigates how modern LLMs can be misused to generate convincing misinformation at scale and its impact on downstream NLP applications. Using GPT-3.5 as a misinformation generator, the authors demonstrate that synthetically-generated false information can significantly degrade ODQA system performance (up to 87% in some…
LLMs & generative AIDetection methods
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability CurvatureThis paper identifies that text sampled from language models tends to occupy negative-curvature regions of the model's log-probability landscape, whereas human-written text occupies more neutral regions. DetectGPT leverages this observation to detect machine-generated text in a zero-shot manner by analyzing probability curvature, without…
LLMs & generative AIDetection methods
A Watermark for Large Language ModelsProposes a watermarking scheme for large language models that embeds imperceptible signals into generated text, enabling detection of machine-generated content without access to model parameters or APIs. The watermark works by promoting a randomized set of "green" tokens during decoding and can be detected using a statistical z-test; the…
LLMs & generative AIDetection methods
Combating Misinformation in the Age of LLMs: Opportunities and ChallengesThis survey systematically reviews how large language models (LLMs) can both combat misinformation and generate it at scale. LLMs offer opportunities for detection, intervention, and attribution through their world knowledge and reasoning abilities, but also pose challenges as they can create deceptive content intentionally or through…
Detection methodsLLMs & generative AI
Linguistic-style-aware Neural Networks for Fake News DetectionHERO (Hierarchical Recursive Neural Network) detects fake news by learning the linguistic style encoded in hierarchical linguistic trees — structures that jointly capture syntactic constituency and rhetorical discourse organization from word to document. Unlike prior handcrafted approaches that extract node-level statistics (word…