mlsanityData to 7 Oct 2026

Fake News Detection After LLM Laundering: Measurement and Explanation

arXiv 2501.18649 ↗First posted 29 Jan 2025cs.CL cs.AI cs.IRcited by 4

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Abstract (arXiv)

With their advanced capabilities, Large Language Models (LLMs) can generate highly convincing and contextually relevant fake news, which can contribute to disseminating misinformation. Though there is much research on fake news detection for human-written text, the field of detecting LLM-generated fake news is still under-explored. This research measures the efficacy of detectors in identifying LLM-paraphrased fake news, in particular, determining whether adding a paraphrase step in the detection pipeline helps or impedes detection. This study contributes: (1) Detectors struggle to detect LLM-paraphrased fake news more than human-written text, (2) We find which models excel at which tasks (evading detection, paraphrasing to evade detection, and paraphrasing for semantic similarity). (3) Via LIME explanations, we discovered a possible reason for detection failures: sentiment shift. (4) We discover a worrisome trend for paraphrase quality measurement: samples that exhibit sentiment shift despite a high BERTSCORE. (5) We provide a pair of datasets augmenting existing datasets with paraphrase outputs and scores. The dataset is available on GitHub

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 39 of its 72 references that are in the map. First posted on arXiv 29 Jan 2025.

Builds on

39 of 72 references.

References · 39

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27 more
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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Modelscited by 12
  2. 2025Toward Verifiable Misinformation Detection: A Multi-Tool LLM Agent Frameworkcited by 6
  3. 2025Adversarial Attacks against Neural Ranking Models via In-Context Learningcited by 4
  4. 2025Agentic Multi-Persona Framework for Evidence-Aware Fake News Detectioncited by 1