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pro vyhledávání: '"Kesen, Ilker"'
The linguistic capabilities of Multimodal Large Language Models (MLLMs) are critical for their effective application across diverse tasks. This study aims to evaluate the performance of MLLMs on the VALSE benchmark, focusing on the efficacy of few-sh
Externí odkaz:
http://arxiv.org/abs/2407.12498
Autor:
Acikgoz, Emre Can, İnce, Osman Batur, Bench, Rayene, Boz, Arda Anıl, Kesen, İlker, Erdem, Aykut, Erdem, Erkut
The integration of Large Language Models (LLMs) into healthcare promises to transform medical diagnostics, research, and patient care. Yet, the progression of medical LLMs faces obstacles such as complex training requirements, rigorous evaluation dem
Externí odkaz:
http://arxiv.org/abs/2404.16621
Autor:
Kesen, Ilker, Pedrotti, Andrea, Dogan, Mustafa, Cafagna, Michele, Acikgoz, Emre Can, Parcalabescu, Letitia, Calixto, Iacer, Frank, Anette, Gatt, Albert, Erdem, Aykut, Erdem, Erkut
With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present Vi
Externí odkaz:
http://arxiv.org/abs/2311.07022
This paper describes our two-stage system for the Euphemism Detection shared task hosted by the 3rd Workshop on Figurative Language Processing in conjunction with EMNLP 2022. Euphemisms tone down expressions about sensitive or unpleasant issues like
Externí odkaz:
http://arxiv.org/abs/2211.04576
Autor:
Ates, Tayfun, Atesoglu, M. Samil, Yigit, Cagatay, Kesen, Ilker, Kobas, Mert, Erdem, Erkut, Erdem, Aykut, Goksun, Tilbe, Yuret, Deniz
Humans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce C
Externí odkaz:
http://arxiv.org/abs/2012.04293
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 4610-4620
How to best integrate linguistic and perceptual processing in multi-modal tasks that involve language and vision is an important open problem. In this work, we argue that the common practice of using language in a top-down manner, to direct visual at
Externí odkaz:
http://arxiv.org/abs/2003.12739
Akademický článek
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Publikováno v:
Concurrency & Computation: Practice & Experience; Nov2018, Vol. 30 Issue 21, pN.PAG-N.PAG, 1p
Autor:
Tayfun Ates, M. Ateşoğlu, Çağatay Yiğit, Ilker Kesen, Mert Kobas, Erkut Erdem, Aykut Erdem, Tilbe Goksun, Deniz Yuret
Publikováno v:
Findings of the Association for Computational Linguistics
Humans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce C
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f5a211efe78296317e1312610117d2d
http://arxiv.org/abs/2012.04293
http://arxiv.org/abs/2012.04293