mlsanityData to 7 Oct 2026

Detecting LLM-assisted writing in scientific communication: Are we there yet?

arXiv 2401.16807 ↗First posted 30 Jan 2024cs.IR cs.AIcited by 4

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Abstract (arXiv)

Large Language Models (LLMs), exemplified by ChatGPT, have significantly reshaped text generation, particularly in the realm of writing assistance. While ethical considerations underscore the importance of transparently acknowledging LLM use, especially in scientific communication, genuine acknowledgment remains infrequent. A potential avenue to encourage accurate acknowledging of LLM-assisted writing involves employing automated detectors. Our evaluation of four cutting-edge LLM-generated text detectors reveals their suboptimal performance compared to a simple ad-hoc detector designed to identify abrupt writing style changes around the time of LLM proliferation. We contend that the development of specialized detectors exclusively dedicated to LLM-assisted writing detection is necessary. Such detectors could play a crucial role in fostering more authentic recognition of LLM involvement in scientific communication, addressing the current challenges in acknowledgment practices.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 6 of its 18 references that are in the map. First posted on arXiv 30 Jan 2024.

Builds on

6 of 18 references.

References · 6

  1. 2023DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Textcited by 207
  2. 2023The Science of Detecting LLM-Generated Textscited by 126
  3. 2023LLMDet: A Third Party Large Language Models Generated Text Detection Toolcited by 44
  4. 2023ConDA: Contrastive Domain Adaptation for AI-generated Text Detectioncited by 37
  5. 2023A Computational Model For Individual Scholars' Writing Style Dynamicscited by 2
  6. 2023The Scientometrics and Reciprocality Underlying Co-Authorship Panels in Google Scholar Profilescited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.