Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366245800> ?p ?o ?g. }
Showing items 1 to 59 of
59
with 100 items per page.
- W4366245800 abstract "Background: ChatGPT is becoming a new reality. Where do we go from here? Objective: to show how we can distinguish ChatGPT-generated publications from counterparts produced by scientist. Methods:By means of a new algorithm, called xFakeBibs, we show the significant difference between ChatGPT-generated fake publications and real publications. Specifically, we triggered ChatGPT to generate 100 publications that were related to Alzheimer’s disease and comorbidity. Using TF-IDF, using the real publications, we constructed a training network of bigrams comprised of 100 publications. Using 10-folds of 100 publications each, we also 10 calibrating networks to derive lower/upper bounds for classifying articles as real or fake. The final step was to test xFakeBibs against each of the ChatGPT-generated articles and predict its class. The algorithm successfully assigned the POSITIVE label for real ones and NEGATIVE for fake ones. Results: When comparing the bigrams of the training set against all the other 10 calibrating folds, we found that the similarities fluctuated between (19%-21%). On the other hand, the mere bigram similarity from the ChatGPT was only (8%). Additionally, when testing how the various bigrams generated from the calibrating 10-folds against ChatGPT we found that all 10 calibrating folds contributed (51%-70%) of new bigrams, while ChatGPT contributed only 23%, which is less than 50% of any of the other 10 calibrating folds. The final classification results using the xFakeBibs set a lower/upper bound of (21.96-24.93) number of new edges to the training mode without contributing new nodes. Using this calibration range, the algorithm predicted 98 of the 100 publications as fake, while 2 articles failed the test and were classified as real publications. Conclusions: This work provided clear evidence of how to distinguish, in bulk ChatGPT-generated fake publications from real publications. Also, we also introduced an algorithmic approach that detected fake articles with a high degree of accuracy. However, it remains challenging to detect all fake records. ChatGPT may seem to be a useful tool, but it certainly presents a threat to our authentic knowledge and real science. This work is indeed a step in the right direction to counter fake science and misinformation." @default.
- W4366245800 created "2023-04-20" @default.
- W4366245800 creator A5029919191 @default.
- W4366245800 date "2023-04-17" @default.
- W4366245800 modified "2023-10-02" @default.
- W4366245800 title "Improving Detection of ChatGPT-Generated Fake Science Using Real Publication Text: Introducing xFakeBibs a Supervised Learning Network Algorithm" @default.
- W4366245800 doi "https://doi.org/10.20944/preprints202304.0350.v2" @default.
- W4366245800 hasPublicationYear "2023" @default.
- W4366245800 type Work @default.
- W4366245800 citedByCount "0" @default.
- W4366245800 crossrefType "posted-content" @default.
- W4366245800 hasAuthorship W4366245800A5029919191 @default.
- W4366245800 hasBestOaLocation W43662458001 @default.
- W4366245800 hasConcept C103278499 @default.
- W4366245800 hasConcept C108757681 @default.
- W4366245800 hasConcept C11413529 @default.
- W4366245800 hasConcept C115961682 @default.
- W4366245800 hasConcept C119857082 @default.
- W4366245800 hasConcept C134306372 @default.
- W4366245800 hasConcept C153180895 @default.
- W4366245800 hasConcept C154945302 @default.
- W4366245800 hasConcept C177264268 @default.
- W4366245800 hasConcept C199360897 @default.
- W4366245800 hasConcept C2777212361 @default.
- W4366245800 hasConcept C33923547 @default.
- W4366245800 hasConcept C41008148 @default.
- W4366245800 hasConcept C77553402 @default.
- W4366245800 hasConceptScore W4366245800C103278499 @default.
- W4366245800 hasConceptScore W4366245800C108757681 @default.
- W4366245800 hasConceptScore W4366245800C11413529 @default.
- W4366245800 hasConceptScore W4366245800C115961682 @default.
- W4366245800 hasConceptScore W4366245800C119857082 @default.
- W4366245800 hasConceptScore W4366245800C134306372 @default.
- W4366245800 hasConceptScore W4366245800C153180895 @default.
- W4366245800 hasConceptScore W4366245800C154945302 @default.
- W4366245800 hasConceptScore W4366245800C177264268 @default.
- W4366245800 hasConceptScore W4366245800C199360897 @default.
- W4366245800 hasConceptScore W4366245800C2777212361 @default.
- W4366245800 hasConceptScore W4366245800C33923547 @default.
- W4366245800 hasConceptScore W4366245800C41008148 @default.
- W4366245800 hasConceptScore W4366245800C77553402 @default.
- W4366245800 hasLocation W43662458001 @default.
- W4366245800 hasLocation W43662458002 @default.
- W4366245800 hasLocation W43662458003 @default.
- W4366245800 hasOpenAccess W4366245800 @default.
- W4366245800 hasPrimaryLocation W43662458001 @default.
- W4366245800 hasRelatedWork W2961085424 @default.
- W4366245800 hasRelatedWork W3046775127 @default.
- W4366245800 hasRelatedWork W3170094116 @default.
- W4366245800 hasRelatedWork W4205958290 @default.
- W4366245800 hasRelatedWork W4285260836 @default.
- W4366245800 hasRelatedWork W4286629047 @default.
- W4366245800 hasRelatedWork W4306321456 @default.
- W4366245800 hasRelatedWork W4306674287 @default.
- W4366245800 hasRelatedWork W4386462264 @default.
- W4366245800 hasRelatedWork W4224009465 @default.
- W4366245800 isParatext "false" @default.
- W4366245800 isRetracted "false" @default.
- W4366245800 workType "article" @default.