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Published August 24, 2026 | Version 1.1.0
Dataset Open

Bibliometric Dataset: Defense Mechanisms in Large Language Models, 2024–2026

Authors/Creators

  • 1. ROR icon Universidad de Santiago de Chile
  • 2. ROR icon Centro Universitario CIFE

Description

Reproducibility package for the bibliometric mapping of defense mechanisms against adversarial attacks on Large Language Models (LLMs), accompanying the manuscript "A Bibliometric Review of Emerging Trends and Strategic Mapping of Defense Mechanisms in Large Language Models: The Q1 Journal Literature, 2024 to Early 2026", under revision at Discover Computing (Springer Nature).

Version 1.1.0 adds the peer-review revision supplement (Zenodo_v1.1_supplement_v1023092026.zip): a reproducible keyword thesaurus (84 variant mappings into 39 canonical groups plus 23 generic index-term removals) in VOSviewer and Bibliometrix formats with its normalization protocol; the revised analysis script (02_bibliometrix_reanalysis.R) and official re-analysis outputs computed with Bibliometrix 4.1.4 on the normalized vocabulary (Callon strategic map, thematic evolution with sensitivity partitions, Benzécri-corrected MCA, hierarchical clustering, and the keyword co-occurrence network in Pajek format); regenerated 300-dpi figures; and a results memo documenting pre- vs post-normalization differences.

Key updates relative to v1.0.0: author-level statistics deduplicated by Scopus Author ID (646 unique authors; the earlier initials-based 516 merged distinct identities); growth reported on complete years only (2024→2025: +727%); Lotka's law reported with fitted exponent (β = 3.96, R² = 0.94); on the normalized vocabulary the motor themes are 'natural language processing' and 'backdoor attacks', with 'large language models' as the basic cross-cutting theme; first two MCA dimensions account for 50.0% of Benzécri-corrected explained inertia (32.95% + 17.05%).

All v1.0.0 files (raw Scopus BibTeX corpus of 137 Q1 articles extracted 13 February 2026, verbatim Boolean queries, original R script, 13 pre-computed tables, 23 figures, README, data dictionary, search strategy, CITATION.cff, CC BY 4.0 license) are included here and in the previous version.

Software requirements: R ≥ 4.3, Bibliometrix 4.1.4, igraph 2.0+, VOSviewer 1.6.20.

Files

Zenodo_Upload_v1.0.zip

Files (14.3 MB)

Name Size
md5:41646ff75e55e1afa9bf7080c19136a8
10.2 MB Preview Download
md5:29c1d80fbc3ab411c231f157feaf2b4d
4.1 MB Preview Download

Additional details

Dates

Collected
2026-02-13
Scopus corpus extraction date

Software

Programming language
R
Development Status
Inactive

References

  • Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  • van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
  • Cuccurullo, C., Aria, M., & Sarto, F. (2016). Foundations and trends in performance management: A twenty-five-year bibliometric analysis in business and public administration domains. Scientometrics, 108(2), 595–611.
  • Aria, M., Cuccurullo, C., D'Aniello, L., Misuraca, M., & Spano, M. (2024). Comparative science mapping: A novel conceptual structure analysis with metadata. Scientometrics. https://doi.org/10.1007/s11192-024-05161-6
  • Aria, M., Misuraca, M., & Spano, M. (2020). Mapping the evolution of social research and data science over 30 years of social indicators research. Social Indicators Research. https://doi.org/10.1007/s11205-020-02281-3