CoChat
Sign Up
Cited by 287Academic integrity and plagiarismEthics and Social Impacts of AIAcademic Writing and Publishing

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Using AI to write scholarly publications

Open access
Hosseini, Mohammad, Rasmussen, Lisa M., Resnik, David B.·2024·Accountability in Research
DOI 10.1080/08989621.2023.2168535PMC PMC10366336
Open this paper in CoChat — free accountOpen PDF

Abstract

Artificial intelligence (AI) natural language processing (NLP) systems, such as OpenAI's generative pre-trained transformer (GPT) model (https://openai. com) or Meta's Galactica (https://galactica.org/)may soon be widely used in many forms of writing, including scientific and scholarly publications (Heaven 2022). 1 While computer programs (such as Microsoft WORD and Grammarly) have incorporated automated text-editing features (such as checking for spelling and grammar) for many years, these programs are not designed to create content.However, new and emerging NLP systems are, which raises important issues for research ethics and research integrity.2 NLP is a way of enabling computers to interact with human language.A key step in NLP, known as tokenization, involves converting unstructured text into structured text suitable for computation.For example, the sentence "The cat sat on the mat" can be structured by tagging its parts: "the [article] cat [noun] sat [verb, past tense] on [preposition] the [article] mat [noun]."Once the parts of the text have been tagged, they can be processed by means of algorithms designed to produce appropriate responses to text (i.e., language generation).Rudimentary NLP-systems, such as the first generation of chatbots that assisted customers on websites, operated according to thousands of human-written rules for processing and generating text.Recent advances in computational speed and capacity and the development of machine-learning (ML) algorithms, such as neural networks, have led to tremendous breakthroughs in NLP (Mitchell 2020).Today's NLP systems use ML to produce and refine statistical models (with billions of parameters) for processing and generating natural language.NLP systems are trained on huge databases (45 terabytes or more) of text available on the internet or other sources.Initial training (or supervised learning) involves giving the system the text and then "rewarding" it for giving correct outputs, as determined by human trainers.3 Over time, NLP systems will reduce their percentage of erroneous outputs and will learn from the data (Mitchell 2020).While NLP systems continue to learn as they receive and process data beyond their initial training data, they do not "know" the meaning or truth-CONTACT

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/08989621.2023.2168535

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 10 references

Research works printed but not linked: 3

  1. Cell Press. 2022. Cell Press Digital Image Guidelines. Last accessed December 15 2022. https://www.cell.com/figureguidelines.
  2. International Committee of Medical Journal Editors. (2023). Preparing a Manuscript for Submission to a Medical Journal. Last accessed January 10 2023. https://www.icmje.org/recommendations/browse/manuscript-preparation/preparing-for-submission.html.
  3. Mitchell, M. 2020. Artificial Intelligence: A Thinking Guide for Humans. New York, NY: Picador.

Cited by

Sorted by citations

Items 201–287 of 287

Related

See all

Metadata

Authors
Hosseini, M., Rasmussen, L. M., & Resnik, D. B.
Affiliations
  • Northwestern University (US)
  • University of North Carolina at Charlotte (US)
  • National Institute of Environmental Health Sciences (US)
Corresponding author
Resnik, David B.
ORCID
Venue
Accountability in Research
Publisher
Informa UK Limited
Published
2023-01-25
Volume
31
Issue
7
Pages
715-723
ISSN
  • 0898-9621 (ISSN-L)
  • 1545-5815
DOI
10.1080/08989621.2023.2168535
PMC
PMC10366336
OpenAlex
W4318035272
Cited by
286
References
10
Citations by year
2026: 56 · 2025: 80 · 2024: 85 · 2023: 65
FWCI
71.56
Type
journal article
Language
en
Open access
Locations
  • Accountability in Research (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institute of Environmental Health Sciences
  • National Center for Advancing Translational Sciences (UL1TR001422)
Topics
  • Social Sciences › Education › Academic integrity and plagiarism
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Library and Information Sciences › Academic Writing and Publishing
Keywords
AI-generated text, scholarly publishing, research ethics, research integrity, generative pre-trained transformer, large language models, tokenization, machine learning, chatbots, supervised learning, authorship attribution
MeSH terms
Scholarly Communication, Artificial Intelligence, Humans, Periodicals as Topic, Publications, Writing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review