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Study: Humans cannot reliably distinguish LLM-generated news from human writing

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv, "Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News," found that participants could not reliably distinguish between news articles written by humans and those generated by large language models (LLMs). The research, which involved over 1,000 participants and collected over 2,300 judgments, indicated that this inability to differentiate holds true across various LLMs, including smaller open-weight models. While self-reported domain expertise improved accuracy, political orientation did not have a significant impact, and cognitive fatigue degraded performance over time. The study concludes that user-side detection is not a viable solution, advocating instead for system-level countermeasures like cryptographic content provenance. AI

IMPACT Suggests a need for system-level content provenance solutions as user-side detection of AI-generated text is unreliable.

RANK_REASON The cluster contains an academic paper detailing a study on LLM-generated content. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Study: Humans cannot reliably distinguish LLM-generated news from human writing

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The cluster contains an academic paper detailing a study on LLM-generated content. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Alexander Loth, Martin Kappes, Marc-Oliver Pahl ·

    Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News

    arXiv:2604.03755v2 Announce Type: replace-cross Abstract: Can humans tell whether a news article was written by a person or a large language model (LLM)? We investigate this question using JudgeGPT, a study platform that independently measures source attribution (human vs. machin…

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