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arXiv AI By Yi Liu

When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems

Read the original on arXiv AI →

The paper investigates how large language models (LLMs) that generate online articles can create a feedback loop where the content they produce becomes input for subsequent generations. By studying a controlled writing process that varies whether published articles are reused as inputs, the authors find that recirculation increases the presence of complete false claims while reducing explicit endorsement of those claims. The study highlights that selective error persistence depends on the interplay between claim content, expressed stance, and downstream reuse, and that interventions can preserve correct content while limiting error reproduction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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