Estimating LLM Prevalence in Scholarly Literature with ChatGPT

Kernkonzepte

The prevalence of LLM-assisted writing in scholarly literature is estimated to be over 1% of all articles, highlighting the impact of tools like ChatGPT.

Zusammenfassung

This study analyzes the use of Large Language Model (LLM) tools like ChatGPT in scholarly communication.

Introduction: Discussion on the rise of LLM tools since late 2022.
LLMs in Scholarly Literature: Overview of LLM capabilities and limitations.
Use of LLMs: Survey results on researchers using writing tools and publisher guidelines.
Identification Methods: Identifying LLM-associated terms and their prevalence increase.
Data Analysis: Analysis of keywords frequency changes from 2015 to 2023.
Combined Terms Analysis: Impact of combining terms on identifying LLM-assisted text.
Estimating Prevalence: Estimation methods for the number of LLM-assisted papers in 2023.
Implications & Future Assessment: Implications for research integrity, model quality, and future trends.

Statistiken

It is estimated that at least 60,000 papers were LLM-assisted in 2023.
The figure for potentially AI-generated text in computer science preprints rose from around a 3% baseline in 2019 through to a peak of over 7% in late 2023.

Zitate

"Tools that are used to improve spelling, grammar, and general editing are not included in the scope of these guidelines." - Wiley Author Services

Wichtige Erkenntnisse aus

ChatGPT "contamination"

by Andrew Gray um arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16887.pdf

Tiefere Fragen

What ethical considerations arise from the undisclosed use of LLMs in academic publishing?

The undisclosed use of Large Language Models (LLMs) in academic publishing raises several ethical concerns. Firstly, there is a lack of transparency and accountability when authors do not disclose their reliance on LLM tools for generating or editing text. This goes against the principles of research integrity and honesty, as readers are entitled to know the extent to which artificial intelligence has been involved in producing scholarly work.
Moreover, the use of LLMs without disclosure can lead to issues related to authorship and intellectual contribution. If significant portions of a paper are generated by an AI tool, questions may arise about who should be credited as the author and how contributions are attributed. This blurring of lines between human-authored content and AI-generated content can create confusion regarding intellectual ownership.
Additionally, there are concerns about the quality and reliability of research that relies heavily on LLM-generated text. Without proper oversight and scrutiny, papers that incorporate AI-generated language may contain errors, biases, or inaccuracies that could mislead readers or compromise the credibility of academic literature.
Overall, undisclosed use of LLMs poses challenges to maintaining research ethics, transparency, and trust within the scholarly community.

How might the increasing prevalence of LLM-generated text impact future research quality?

The growing prevalence of Large Language Model (LLM)-assisted text in academic research has implications for future research quality. One major concern is related to authenticity and originality in scholarly work. As more researchers turn to LLM tools for writing assistance or content generation, there is a risk that papers will become homogenized in terms of style and language usage. This could lead to a loss of diversity in writing styles across disciplines and potentially diminish creativity in scientific communication.
Furthermore, if a significant portion of academic papers contains undisclosed LLM-assisted text, there is a risk that errors or inaccuracies introduced by these models may go unnoticed. This could undermine the reliability and validity of research findings presented in such papers.
Moreover, as mentioned in the context provided earlier about model collapse where artificially generated text outweighs real data leading to low-quality results; this scenario becomes increasingly likely with widespread adoption but insufficient oversight over LMM usage.
In summary,
the increasing prevalence
of
LLM-assisted
text
could
potentially
impact
research quality by compromising authenticity,
originality,
diversity
in writing styles,
and overall accuracy

How can publishers effectively address undisclosed LLM use to maintain research integrity?

Publishers play a crucial role in upholding standards for transparency
and integrity
in academic publishing.
To address undisclosed Large Language Model (LLM) use,
publishers should consider implementing clear guidelines requiring authors
to disclose any assistance received from AI tools during manuscript preparation.
This disclosure should extend beyond simple copyediting assistance
to include any substantial contributions made by AI models towards generating content.
By making such disclosures mandatory,
publishers can ensure greater accountability among authors
and promote transparency within scholarly communication.
Another approach would be for publishers
to develop mechanisms
for detecting potential instances
of Large Language Model (LLM)
use in submitted manuscripts.
This could involve using automated tools
or conducting manual checks
to identify patterns or markers
that suggest the involvement of an LLM in the writing process.
Additionally,
publishers can engage with authors during the submission and review process to raise awareness about the importance of disclosing LLM use and upholding research integrity.
By taking proactive steps to address undisclosed LLM use,
publishers can help safeguard the trustworthiness of scholarly literature and maintain high standards for research integrity across various disciplines