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PLLuM is designed to address the needs not only of scientists and enterprises, but also Polish citizens. The model aims to deliver innovative solutions that benefit Polish society directly. One of them is a Polish-language smart assistant that will facilitate access to public services, both electronically and in person. By enabling the formulation of queries in natural language, the assistant caters to the needs of those considered digitally excluded. And that is merely the beginning. The collaborative efforts of the Polish research, business and public sectors have the potential to unlock endless possibilities. |
PLLuM
Project factsheet
English name: |
PLLuM – Polish Large Language Model |
Polish name: |
Odpowiedzialny rozwój otwartego dużego modelu językowego PLLuM (Polish Large Language Universal Model) w celu wspierania technologii przełomowych w sektorze publicznym i gospodarczym, w tym otwartego, polskojęzycznego inteligentnego asystenta petenta |
Funding: |
Ministry of Digitization |
Grant number: |
Grant Agreement 1/WI/DBiI/2023. Decision of the Prime Minister DGT.06124.6.2023 of 8 January 2024 |
Duration: |
1 July 2013 – 30 June 2015 (part 1), 1 July 2016 – 30 June 2018 (part 2), 1 July 2018 – 30 June 2021 (part 3), 1 March 2022 – 31 December 2023 (part 4 / CLARIN Q) |
Project Web page: |
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Consortium leader: |
Wrocław University of Science and Technology |
PLLuM Consortium
Wrocław University of Science and Technology (coordinator: Maciej Piasecki)
Institute of Computer Science, Polish Academy of Sciences (coordinator: Maciej Ogrodniczuk)
Institute of Slavic Studies, Polish Academy of Sciences (coordinator: Roman Roszko)
University of Łódź (coordinator: Piotr Pęzik)
NASK National Research Institute (coordinator: Agnieszka Karlińska)
National Information Processing Institute, National Research Institute (coordinator: Marek Kozłowski)
Project desrciption
The consortium’s goal is to develop the first open Polish LLM and an associated smart assistant. The project will adhere to ethical and responsible best practices in AI, incorporating data representativeness, transparency and fairness.