Highlights:
- Researchers Mingmeng Geng and Thierry Poibeau challenge the definition of ‘LLM-generated text’.
- The study argues current detection methods fail to capture the real-world complexity of text generation.
- Human editing and LLM influence blur the line between AI-assisted and human-written content.
- Detection tools should be used cautiously as references, not absolute indicators.
TLDR:
A 2025 study by Mingmeng Geng and Thierry Poibeau questions the reliability and meaning of detecting large language model (LLM)-generated text. The researchers argue that the concept itself lacks a fixed definition, urging caution in interpreting detection metrics and emphasizing the evolving human-AI writing landscape.
With the rapid adoption of large language models (LLMs) in education, journalism, and industry, the boundary between human and machine-generated text has become a pressing concern. In their recent paper titled ‘On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?’, researchers Mingmeng Geng (https://arxiv.org/search/cs?searchtype=author&query=Geng,+M) and Thierry Poibeau (https://arxiv.org/search/cs?searchtype=author&query=Poibeau,+T) delve into this critical question. They note that while AI-detection systems have proliferated, the term ‘LLM-generated text’ itself lacks a consistent or precise definition, making reliable detection increasingly elusive.
Their study, posted on arXiv under Computation and Language (cs.CL), highlights the difficulties that come from a growing diversity of LLM architectures, training data, and interaction contexts. According to the authors, most detection tools only capture a narrow subset of the possible outputs that LLMs can produce. Moreover, as humans edit, paraphrase, or expand on AI-generated drafts, the resulting hybrid texts blend seamlessly into human-authored prose. The authors emphasize that these complex interactions blur the line between human creativity and machine assistance, leading to both ethical and methodological challenges for educators, policymakers, and AI researchers.
Geng and Poibeau argue that traditional benchmarks and evaluation protocols underestimate these real-world challenges. Detection scores often fail to reflect nuanced use cases, leading to misunderstandings about their accuracy. As such, they propose viewing AI-detection outputs as indicators rather than definitive proof of authorship. Their work calls for a reconsideration of how the research community conceptualizes and measures ‘AI-generated text’, warning that without updated frameworks, trust in these detection tools may continue to erode. The implications reach beyond academia: from misinformation monitoring to university integrity systems, understanding what truly qualifies as LLM-generated is essential for a fair and transparent digital environment.
Source:
Source:
Mingmeng Geng & Thierry Poibeau (2025). ‘On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?’ arXiv:2510.20810v1 [cs.CL], https://doi.org/10.48550/arXiv.2510.20810