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Projekt | DisAI

Laufzeit:
Improving scientific excellence and creativity in combating disinformation with artificial intelligence and language technologies

Improving scientific excellence and creativity in combating disinformation with artificial intelligence and language technologies

Forschungsthemen

In the DisAI project, the Kempelen Institute of Intelligent Technologies, DFKI, University of Copenhagen and CERTH joined forces to improve scientific excellence of KInIT in AI and language technologies to fight disinformation.

DFKI, University of Copenhagen and CERTH will be KInIT’s partners and mentors in the project focused exclusively on increasing KInIT’s scientific excellence, expertise and competences.

The R&I ecosystem of Slovakia suffers from a lack of scientific excellence and industry-academia collaboration. Gross Domestic Expenditure on R&I from the private sector in 2020 was 0.5 % of the Slovak GDP, being one of the lowest in the EU. Challenged by the lack of internationalisation, “academic inbreeding” and brain drain (second highest in EU), Slovakia continually ranks on the tail of participation and success rates in EU funding programmes.

The main objectives of the project are improving scientific excellence of KInIT in the selected areas of AI and LT, and strengthening research management and administrative skills and support for excellent research at KInIT.

We focus on disinformation combating, which we consider one of the most important societal challenges to tackle. We already have a rising research track/record and involvement in international initiatives in the area of information disorders. Strengthening KInIT’s competencies will be achieved by building its capacity in three focus areas:

  • Multilingual Language Technologies Multimodal Natural Language Processing Trustworthy Artificial Intelligence

Partner

Kempelen Institute of Intelligent Technologies (KInIT)The Centre for Research & Technology, Hellas (CERTH)University of Copenhagen

Publikationen zum Projekt

  1. Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters

    Daniil Gurgurov; Mareike Hartmann; Simon Ostermann

    In: Russa Biswas; Lucie-Aimée Kaffee; Oshin Agarwal; Pasquale Minervini; Sameer Singh; Gerard de Melo (Hrsg.). Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024). Workshop on Knowledge Graphs and Large Language Models (KaLLM-2024), August 15, Bangkok, Thailand, Pages 63-74, Association for Computational Linguistics, 2024.

Fördergeber

EU - Europäische Union

EU - Europäische Union