Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Won, Inho"'
Autor:
Lim, Hyeonseok, Shin, Dongjae, Song, Seohyun, Won, Inho, Kim, Minjun, Yuk, Junghun, Jang, Haneol, Lim, KyungTae
We propose the VLR-Bench, a visual question answering (VQA) benchmark for evaluating vision language models (VLMs) based on retrieval augmented generation (RAG). Unlike existing evaluation datasets for external knowledge-based VQA, the proposed VLR-B
Externí odkaz:
http://arxiv.org/abs/2412.10151
Autor:
Shin, Dongjae, Lim, Hyeonseok, Won, Inho, Choi, Changsu, Kim, Minjun, Song, Seungwoo, Yoo, Hangyeol, Kim, Sangmin, Lim, Kyungtae
The impressive development of large language models (LLMs) is expanding into the realm of large multimodal models (LMMs), which incorporate multiple types of data beyond text. However, the nature of multimodal models leads to significant expenses in
Externí odkaz:
http://arxiv.org/abs/2403.11399
Autor:
Choi, ChangSu, Jeong, Yongbin, Park, Seoyoon, Won, InHo, Lim, HyeonSeok, Kim, SangMin, Kang, Yejee, Yoon, Chanhyuk, Park, Jaewan, Lee, Yiseul, Lee, HyeJin, Hahm, Younggyun, Kim, Hansaem, Lim, KyungTae
Large language models (LLMs) use pretraining to predict the subsequent word; however, their expansion requires significant computing resources. Numerous big tech companies and research institutes have developed multilingual LLMs (MLLMs) to meet curre
Externí odkaz:
http://arxiv.org/abs/2403.10882
Publikováno v:
Pacific Affairs, 2017 Sep 01. 90(3), 505-533.
Externí odkaz:
https://www.jstor.org/stable/44874508
