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CORDIS - Forschungsergebnisse der EU
CORDIS

AI-CODE - AI services for COntinuous trust in emerging Digital Environments

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

Prototyping the disinformation detection and next generation of social media services (öffnet in neuem Fenster)

Initial versions of the disinformation detection services focusing on next generation social media, as carried out in T4.1

Prototype version of Dynamic Simulator for Media Professionals (öffnet in neuem Fenster)

Initial -prototype of the interactive simulator for media professionals allowing users to observe selected functionalities of the simulator and give feedback regarding its design.

Prototyping the Media Asset Annotation and Management (MAAM) service (öffnet in neuem Fenster)

Initial versions of services that were built and prototyped in T4.3.

Prototype version of the Personal Companion for media professionals service (öffnet in neuem Fenster)

Initial version of the Personal Companion for understanding disinformation for media professionals service.

Prototyping the Trustability and credibility assessment services (öffnet in neuem Fenster)

First prototype of the Trustability and credibility assessment services produced in T4.2

Prototyping transparency services for AI-model cards (öffnet in neuem Fenster)

Initial versions of transparency services for AI-model cards carried out in T6.2

Quality Plan (öffnet in neuem Fenster)

This deliverable will state all the quality procedures to guarantee a successful project implementation.

Adaptation of existing credibility and trust assessment and disinformation detection tools to next-generation social media and AI-generated content (öffnet in neuem Fenster)

On the basis of T2.4, this deliverable will complement previous two deliverables and provide recommendations for development of AI-CODE services.

Data Management Plan (öffnet in neuem Fenster)

This deliverable will describe how the AI-CODE project manages data throughout its life cycle, in order to be compliant to the regulatory framework.

Functional specifications and technical requirements (öffnet in neuem Fenster)

Description of the necessary functionalities and technical requirements of the AI-CODE services based on results from tasks T3.3 und T3.4.

Report on the assessment of the role of novel text and image generative AI methodologies in producing and combating disinformation (öffnet in neuem Fenster)

This deliverable will summarise the research performed in T2.2 and T2.3 and describe positive as well as negative consequences of generative AI adopted in social media.

Impact master plan (öffnet in neuem Fenster)

Document detailing the project dissemination, and communication plan, outlining the target groups and relevant tailored outreach strategies and the project communication channels.

User-centred requirements definition (öffnet in neuem Fenster)

Requirements definition for the defined user groups and use case scenarios based on results of the analysis in T3.1, T3.2 and T3.4.

Report on technological and socio-behavioural characterization of next-generation social media (öffnet in neuem Fenster)

This deliverable will summarise the research performed in T2.1 and outline the emerging trust and disinformation risks.

Veröffentlichungen

LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs? (öffnet in neuem Fenster)

Autoren: Čegiň, Ján; Simko, Jakub; Brusilovsky, Peter
Veröffentlicht in: Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (NAACL 2025), 2025
Herausgeber: Association for Computational Linguistics
DOI: 10.48550/ARXIV.2408.16502

Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation (öffnet in neuem Fenster)

Autoren: Jan Cegin, Branislav Pecher, Jakub Simko, Ivan Srba, Maria Bielikova, Peter Brusilovsky
Veröffentlicht in: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024
Herausgeber: Association for Computational Linguistics
DOI: 10.18653/v1/2024.acl-long.710

Authorship Obfuscation in Multilingual Machine-Generated Text Detection (öffnet in neuem Fenster)

Autoren: Macko, Dominik; Moro, Robert; Uchendu, Adaku; Srba, Ivan; Lucas, Jason Samuel; Yamashita, Michiharu; Tripto, Nafis Irtiza; Lee, Dongwon; Simko, Jakub; Bielikova, Maria
Veröffentlicht in: Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
Herausgeber: Association for Computational Linguistics
DOI: 10.48550/arXiv.2401.07867

SIDBench: A Python framework for reliably assessing synthetic image detection methods (öffnet in neuem Fenster)

Autoren: Manos Schinas, Symeon Papadopoulos
Veröffentlicht in: 3rd ACM International Workshop on Multimedia AI against Disinformation, 2024
Herausgeber: ACM
DOI: 10.1145/3643491.3660277

ModaFact: Multi-paradigm Evaluation for Joint Event Modality and Factuality Detection

Autoren: Rovera, Marco; Cristoforetti, Serena; Tonelli,Sara
Veröffentlicht in: Proceedings of the 31st International Conference on Computational Linguistics, 2025
Herausgeber: Association for Computational Linguistics

A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts (öffnet in neuem Fenster)

Autoren: Tripto, Nafis Irtiza; Venkatraman, Saranya; Macko, Dominik; Moro, Robert; Srba, Ivan; Uchendu, Adaku; Le, Thai; Lee, Dongwon
Veröffentlicht in: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024
Herausgeber: Association for Computational Linguistics
DOI: 10.48550/arXiv.2311.08374

MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts (öffnet in neuem Fenster)

Autoren: Dominik Macko, Jakub Kopál, Robert Moro, Ivan Srba
Veröffentlicht in: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025
Herausgeber: Association for Computational Linguistics
DOI: 10.48550/ARXIV.2406.12549

KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection (öffnet in neuem Fenster)

Autoren: Spiegel, Michal; Macko, Dominik
Veröffentlicht in: Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), 2024
Herausgeber: Association for Computational Linguistics
DOI: 10.48550/arXiv.2402.13671

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