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BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models

arXiv (Cornell University)(2024)10.48550/arxiv.2410.09079Source: DataRank Database

BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models is a research paper published in arXiv (Cornell University) (2024). On theSindex it has a DataRank of 0.

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Enrichment:Funding (3 grants)OA: greenTopics (26)IDs (PubMed, OpenAlex)

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Authors (7)

Jiaqi Wang ORCID,Han Liu ORCID,Parminder Bhatia ORCID,Cao Xiao ORCID,Ting Wang ORCID

Live enrichment

PubMed:30945415 OpenAlex:https://openalex.org/W4403564241

National Science Foundation

Grant: 2212323

SaTC: CORE: Small: Understanding and Mitigating the Security Risks of AutoML

National Institutes of Health

Grant: 5R01AG077016-03

SCH: AI-Enhanced Multimodal Sensor-on-a-chip for Alzheimer's Disease Detection

National Science Foundation

Grant: 2238275

CAREER: Automated Multimodal Learning for Healthcare

greenLicense: cc-by

repositoryhttps://arxiv.org/pdf/2410.09079

repositoryhttp://arxiv.org/abs/2410.09079

repositoryhttps://doi.org/10.48550/arxiv.2410.09079

https://doi.org/10.18653/v1/2024.findings-emnlp.437

https://dx.doi.org/10.48550/arxiv.2410.09079

Fields of Study

Natural Language Processing TechniquesTopic ModelingSpeech Recognition and SynthesisComputation and Language (cs.CL)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciences0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Computer scienceFine-tuningRangingLanguage modelMathematical optimizationArtificial intelligenceMathematicsPhysicsParticle physicsFOS: Computer and information sciencesComputer Science - Machine LearningComputer Science - Computation and LanguageArtificial Intelligence (cs.AI)Computer Science - Artificial IntelligenceComputation and Language (cs.CL)ArticleMachine Learning (cs.LG)