https://biblio.ugent.be/ Ghent University Academic Bibliography 2000-01-01T00:00+00:00 1 monthly https://biblio.ugent.be/publication/521729 Stroobandt, Dirk Van Campenhout, Jan Dambre, Joni 2003 application/pdf https://biblio.ugent.be/publication/521729 http://hdl.handle.net/1854/LU-521729 https://biblio.ugent.be/publication/521729/file/1875059 dut eng Universiteit Gent. Faculteit Toegepaste Wetenschappen info:eu-repo/semantics/openAccess Technology and Engineering Predictie van interconnectie-eigenschappen van digitale schakelingen voor de exploratie van ontwerpkeuzes en technologieën dissertation info:eu-repo/semantics/doctoralThesis info:eu-repo/semantics/publishedVersion https://biblio.ugent.be/publication/01GP37D6W8299E7AE1YVZGDV5S Vandeghinste, Vincent Van Dyck, Bob De Coster, Mathieu Goddefroy, Maud Dambre, Joni 2022 We are presenting the Belgian Federal COVID-19 corpus, nicknamed the BeCoS (Belgian Covid Sign language) corpus. It consists of the entire archive of official press conferences from the Belgian Federal Government concerning the COVID-19 pandemic. The speakers speak mostly in Dutch or French and occasionally in German, and nearly all speech is accompanied by a deaf signer who performs live interpreting from what is being said. We have preprocessed the corpus with speaker diarisation, applied Belgian Dutch ASR, and post-ASR language identification and punctuation prediction as well as signer diarisation, sign language identification and sign language keypoint recognition. The corpus is made publicly available. application/pdf https://biblio.ugent.be/publication/01GP37D6W8299E7AE1YVZGDV5S http://hdl.handle.net/1854/LU-01GP37D6W8299E7AE1YVZGDV5S https://biblio.ugent.be/publication/01GP37D6W8299E7AE1YVZGDV5S/file/01GP37GVNB9NTCZNNGKRJWNFMS eng info:eu-repo/semantics/openAccess COMPUTATIONAL LINGUISTICS IN THE NETHERLANDS JOURNAL ISSN: 2211-4009 Languages and Literatures BeCoS corpus : Belgian Covid-19 Sign language corpus : a corpus for training sign language recognition and translation journalArticle info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://biblio.ugent.be/publication/01JVW243A2466QF51W74JW9DEH Kurosu, Masaaki Hashizume, Ayako Cazalets, Tanguy Dambre, Joni 2025 This paper introduces the Word Synchronization Challenge, a novel benchmark to evaluate large language models (LLMs) in Human-Computer Interaction (HCI). This benchmark utilizes a dynamic game-like framework to test LLMs’ ability to mimic human cognitive processes through word associations. By simulating complex human interactions, it assesses how LLMs interpret and align with human thought patterns during conversational exchanges, essential for effective social partnerships in HCI. Initial findings highlight the influence of model sophistication on performance, offering insights into the models’ capabilities to engage in meaningful social interactions and adapt behaviors in human-like manners. This research advances understanding of LLMs’ potential to replicate or diverge from human cognitive functions, paving the way for more nuanced and empathetic human-machine collaborations. application/pdf https://biblio.ugent.be/publication/01JVW243A2466QF51W74JW9DEH http://hdl.handle.net/1854/LU-01JVW243A2466QF51W74JW9DEH http://doi.org/10.1007/978-3-031-93864-1_1 https://biblio.ugent.be/publication/01JVW243A2466QF51W74JW9DEH/file/01JVW25Y0WZ9N0KE5QK64K11BW eng Springer info:eu-repo/semantics/openAccess HUMAN-COMPUTER INTERACTION, HCI 2025, PT V ISSN: 0302-9743 ISSN: 1611-3349 ISBN: 9783031938634 ISBN: 9783031938641 Technology and Engineering Human Centered AI (HCAI) Explainable AI (XAI) Benchmark Theory of Mind Word associations Word synchronization challenge : a benchmark for word association responses for large language models conference info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion