Szczegóły publikacji
Opis bibliograficzny
Fact-checking generative AI: ontology-driven biological graphs for disease-gene link verification / Ahmed Abdeen Hamed, Alessandro Crimi, Byung Suk Lee, Magdalena M. Misiak // W: Computational Science – ICCS 2024 : 24th International Conference : Malaga, Spain, July 2–4, 2024 : proceedings, Pt. 4 / eds. Leonardo Franco, [et al.]. — Cham : Springer, cop. 2024. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 14835). — ISBN: 978-3-031-63771-1; e-ISBN: 978-3-031-63772-8. — S. 130–137. — Bibliogr., Abstr. — Publikacja dostępna online od: 2024-06-28. — A. Crimi - afiliacja: Sano Centre for Computational Medicine, Cracow
Autorzy (4)
- Hamed Ahmed Abdeen
- Crimi Alessandro
- Lee Byung Suk
- Misiak Magdalena M.
Dane bibliometryczne
| ID BaDAP | 157778 |
|---|---|
| Data dodania do BaDAP | 2025-01-30 |
| DOI | 10.1007/978-3-031-63772-8_12 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2024 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Since the launch of various generative AI tools, scientists have been striving to evaluate their capabilities and contents, in the hope of establishing trust in their generative abilities. Regulations and guidelines are emerging to verify generated contents and identify novel uses. we aspire to demonstrate how ChatGPT claims are checked computationally using the rigor of network models. We aim to achieve fact-checking of the knowledge embedded in biological graphs that were contrived from ChatGPT contents at the aggregate level. We adopted a biological networks approach that enables the systematic interrogation of ChatGPT’s linked entities. We designed an ontology-driven fact-checking algorithm that compares biological graphs constructed from approximately 200,000 PubMed abstracts with counterparts constructed from a dataset generated using the ChatGPT-3.5 Turbo model. In 10-samples of 250 randomly selected records a ChatGPT dataset of 1000 “simulated” articles , the fact-checking link accuracy ranged from 70% to 86%. This study demonstrated high accuracy of aggregate disease-gene links relationships found in ChatGPT-generated texts.
