Fake News Detection After LLM Laundering: Measurement and Explanation
This addresses the problem of misinformation spread via LLM-generated fake news for online content moderators and detection systems, though it is incremental as it builds on existing detection research.
The study measured the difficulty of detecting LLM-paraphrased fake news compared to human-written text, finding that detectors struggle more with LLM-generated content, and identified sentiment shift as a key reason for detection failures despite high paraphrase quality scores.
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