John von Neumann University

output

20202026

most citedInvestigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems

36 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

A. Czirják2 · h 1
András Hajdu2 · h 8
Péter Földi2
P. Pollner2 · h 7
Róbert Lakatos2 · h 3
Szabolcs Hack2 · h 4
Tamás Joó2 · h 3
Ákos Gombkötő1
András London1 · h 2
András Pluhár1 · h 2
B. G. Pusztai1 · h 2
B.G. Pusztai1

collaborating institutions

University of SzegedHU3 papers
ELI-HU Research and Development Non-ProfitHU2 papers
Semmelweis UniversityHU2 papers
University of DebrecenHU2 papers
Babeș-Bolyai UniversityRO1 paper
Corvinus University of BudapestHU1 paper
ELTE Centre for Economic and Regional StudiesHU1 paper
Hungarian Research NetworkHU1 paper
HUN-REN Institute for Computer Science and ControlHU1 paper
HUN-REN Wigner Research Centre for PhysicsHU1 paper
Institute of EconomicsRU1 paper
University of PannoniaHU1 paper

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cs.CL2024★ 36 cited

Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems

Robert Lakatos, Peter Pollner, Andras Hajdu +1

The development of generative large language models (G-LLM) opened up new opportunities for the development of new types of knowledge-based systems similar to ChatGPT, Bing, or Gem…