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Buda Health Center

Hungary

6 papers here69 citations across 6
fields
  • cs.RO2
  • cs.CL1
  • cs.CV1
  • physics.chem-ph1
  • quant-ph1
ROR 00yx04352OpenAlex

affiliations via OpenAlex

output
20192025
most citedAchieving Peak Performance for Large Language Models: A Systematic Review

38 citations

researchers with a paper here
  • S. Szénási2 · h 15
  • A. Baghban1 · h 45
  • A. Bemani1 · h 22
  • Ádám Wolf1 · h 4
  • A. Mosavi1 · h 74
  • A. Várkonyi-Kóczy1 · h 28
  • B. Parkinson1 · h 4
  • Erika Bene1 · h 1
  • Gábor Drótos1 · h 2
  • Gábor Kertész1 · h 9
  • Istv'an M'arton1 · h 2
  • Kaziwa Saleh1 · h 3
collaborating institutions
  • Amirkabir University of TechnologyIR1 paper
  • HUN-REN Institute for Computer Science and ControlHU1 paper
  • HUN-REN Institute for Nuclear ResearchHU1 paper
  • Innova (Hungary)HU1 paper
  • Institute for Cross-Disciplinary Physics and Complex SystemsES1 paper
  • Islamic Azad University MahshahrIR1 paper
  • J. Selye UniversitySK1 paper
  • Obuda UniversityHU1 paper
  • Oxford Brookes UniversityGB1 paper
  • Petroleum University of TechnologyIR1 paper
  • Takeda (Austria)AT1 paper
  • Ton Duc Thang UniversityVN1 paper
Showing cs.CLShow all

1 paper · 1 filter

cs.CL2024★ 38 cited

Achieving Peak Performance for Large Language Models: A Systematic Review

Zhyar Rzgar K Rostam, Sándor Szénási, Gábor Kertész

In recent years, large language models (LLMs) have achieved remarkable success in natural language processing (NLP). LLMs require an extreme amount of parameters to attain high per…

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