EXPLORING COMPRESSION STRATEGIES FOR LARGE LANGUAGE MODELS TOWARDS EFFICIENT ARTIFICIAL INTELLIGENCE IMPLEMENTATIONS
- DOI
- 10.5281/zenodo.21825592
- Published
- 2024
- Container
- Not recorded
- Publisher
- Romanian-American University Publishing House
- Open access
- yes
Credibility signals
limited evidence Score 47/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- supportingPublication license: A publication license was supplied: https://creativecommons.org/licenses/by/4.0/legalcode. Presence improves reuse transparency, not research validity.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.5281/zenodo.21825592,
title = {EXPLORING COMPRESSION STRATEGIES FOR LARGE LANGUAGE MODELS TOWARDS EFFICIENT ARTIFICIAL INTELLIGENCE IMPLEMENTATIONS},
author = {Doinita SENDRE and Dana-Mihaela PETROSANU and Alexandru PIRJAN},
year = {2024},
doi = {10.5281/zenodo.21825592},
url = {https://doi.org/10.5281/zenodo.21825592}
}RIS
TY - JOUR TI - EXPLORING COMPRESSION STRATEGIES FOR LARGE LANGUAGE MODELS TOWARDS EFFICIENT ARTIFICIAL INTELLIGENCE IMPLEMENTATIONS AU - Doinita SENDRE AU - Dana-Mihaela PETROSANU AU - Alexandru PIRJAN PY - 2024 DO - 10.5281/zenodo.21825592 UR - https://doi.org/10.5281/zenodo.21825592 ER -
APA
SENDRE, D., PETROSANU, D., & PIRJAN, A. (2024). EXPLORING COMPRESSION STRATEGIES FOR LARGE LANGUAGE MODELS TOWARDS EFFICIENT ARTIFICIAL INTELLIGENCE IMPLEMENTATIONS. https://doi.org/10.5281/zenodo.21825592