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Probing Deep Learning Models by Logic

Public support

  • Provider

    Czech Science Foundation

  • Programme

    Standard projects

  • Call for proposals

    SGA0202600001

  • Main participants

    České vysoké učení technické v Praze / Fakulta elektrotechnická

  • Contest type

    VS - Public tender

  • Contract ID

    26-23758S

Alternative language

  • Project name in Czech

    Probing Deep Learning Models by Logic

  • Annotation in Czech

    Deep learning may be paving the way toward general artificial intelligence. While its progress is impressive, it is also concerning that we are expected to place blind trust in models that function as black boxes—networks of billions of weights, where their creators could potentially embed hidden and undesirable “surprises”. In this project, we will contribute to the overall goal of making deep learning models more transparent by developing tools for probing and benchmarking them. For that we will use first-order logic and non-monotonic logics. We propose to contribute to the state of the art along the following axes: (i) Evaluating deep learning models and/or the algorithms used to train them, and (ii) Extracting the implicit logical knowledge in these models and reasoning with that knowledge. Our focus will be on two major model types: graph neural networks (GNNs) and large language models (LLMs).

Scientific branches

  • R&D category

    ZV - Basic research

  • OECD FORD - main branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

  • OECD FORD - secondary branch

  • OECD FORD - another secondary branch

  • CEP - equivalent branches <br>(according to the <a href="http://www.vyzkum.cz/storage/att/E6EF7938F0E854BAE520AC119FB22E8D/Prevodnik_oboru_Frascati.pdf">converter</a>)

    AF - Documentation, librarianship, work with information<br>BC - Theory and management systems<br>BD - Information theory<br>IN - Informatics

Solution timeline

  • Realization period - beginning

    Jan 1, 2026

  • Realization period - end

    Dec 31, 2028

  • Project status

    Z - Beginning multi-year project

  • Latest support payment

Data delivery to CEP

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

  • Data delivery code

    CEP26-GA0-GA-R

  • Data delivery date

    May 6, 2026

Finance

  • Total approved costs

    10,356 thou. CZK

  • Public financial support

    10,356 thou. CZK

  • Other public sources

    0 thou. CZK

  • Non public and foreign sources

    0 thou. CZK