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pro vyhledávání: '"Karamolegkou, Antonia"'
Autor:
Karamolegkou, Antonia, Rust, Phillip, Cao, Yong, Cui, Ruixiang, Søgaard, Anders, Hershcovich, Daniel
Large vision-language models (VLMs) can assist visually impaired people by describing images from their daily lives. Current evaluation datasets may not reflect diverse cultural user backgrounds or the situational context of this use case. To address
Externí odkaz:
http://arxiv.org/abs/2407.06177
Pretrained large Vision-Language models have drawn considerable interest in recent years due to their remarkable performance. Despite considerable efforts to assess these models from diverse perspectives, the extent of visual cultural awareness in th
Externí odkaz:
http://arxiv.org/abs/2402.06015
Autor:
Cao, Yong, Kementchedjhieva, Yova, Cui, Ruixiang, Karamolegkou, Antonia, Zhou, Li, Dare, Megan, Donatelli, Lucia, Hershcovich, Daniel
Building upon the considerable advances in Large Language Models (LLMs), we are now equipped to address more sophisticated tasks demanding a nuanced understanding of cross-cultural contexts. A key example is recipe adaptation, which goes beyond simpl
Externí odkaz:
http://arxiv.org/abs/2310.17353
Language models may memorize more than just facts, including entire chunks of texts seen during training. Fair use exemptions to copyright laws typically allow for limited use of copyrighted material without permission from the copyright holder, but
Externí odkaz:
http://arxiv.org/abs/2310.13771
The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (O
Externí odkaz:
http://arxiv.org/abs/2310.06458
Over the years, many researchers have seemingly made the same observation: Brain and language model activations exhibit some structural similarities, enabling linear partial mappings between features extracted from neural recordings and computational
Externí odkaz:
http://arxiv.org/abs/2306.05126
Autor:
Li, Jiaang, Karamolegkou, Antonia, Kementchedjhieva, Yova, Abdou, Mostafa, Lehmann, Sune, Søgaard, Anders
Large language models (LLMs) have complicated internal dynamics, but induce representations of words and phrases whose geometry we can study. Human language processing is also opaque, but neural response measurements can provide (noisy) recordings of
Externí odkaz:
http://arxiv.org/abs/2306.01930
Autor:
Karamolegkou, Antonia
Researchers writing scientific articles summarize their work in the abstracts mentioning the final outcome of their study. Argumentation mining can be used to extract the claim of the researchers as well as the evidence that could support their claim
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448855
Autor:
Due, Jeppe Klok, Pedersen, Marianne Giørtz, Antonsen, Sussie, Rommedahl, Joen, Agerbo, Esben, Mortensen, Preben Bo, Sørensen, Henrik Toft, Lotz, Jonas Færch, Piqueras, Laura Cabello, Fierro, Constanza, Karamolegkou, Antonia, Igel, Christian, Rust, Phillip, Søgaard, Anders, Pedersen, Carsten Bøcker
Publikováno v:
Scandinavian Journal of Public Health; Jun2024, Vol. 52 Issue 4, p528-538, 11p
Akademický článek
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