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pro vyhledávání: '"Lee, Minhwa"'
Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants
Large Language Models (LLMs) have assisted humans in several writing tasks, including text revision and story generation. However, their effectiveness in supporting domain-specific writing, particularly in business contexts, is relatively less explor
Externí odkaz:
http://arxiv.org/abs/2406.18675
Publikováno v:
LREC-COLING 2024
Prior research on Twitter (now X) data has provided positive evidence of its utility in developing supplementary health surveillance systems. In this study, we present a new framework to surveil public health, focusing on mental health (MH) outcomes.
Externí odkaz:
http://arxiv.org/abs/2402.13452
Autor:
Das, Debarati, De Langis, Karin, Martin-Boyle, Anna, Kim, Jaehyung, Lee, Minhwa, Kim, Zae Myung, Hayati, Shirley Anugrah, Owan, Risako, Hu, Bin, Parkar, Ritik, Koo, Ryan, Park, Jonginn, Tyagi, Aahan, Ferland, Libby, Roy, Sanjali, Liu, Vincent, Kang, Dongyeop
This work delves into the expanding role of large language models (LLMs) in generating artificial data. LLMs are increasingly employed to create a variety of outputs, including annotations, preferences, instruction prompts, simulated dialogues, and f
Externí odkaz:
http://arxiv.org/abs/2401.14698
Collecting diverse human opinions is costly and challenging. This leads to a recent trend in collaborative efforts between humans and Large Language Models (LLMs) for generating diverse data, offering potential scalable and efficient solutions. Howev
Externí odkaz:
http://arxiv.org/abs/2311.09799
Large Language Models are cognitively biased judges. Large Language Models (LLMs) have recently been shown to be effective as automatic evaluators with simple prompting and in-context learning. In this work, we assemble 15 LLMs of four different size
Externí odkaz:
http://arxiv.org/abs/2309.17012
Visual Word Sense Disambiguation (VWSD) is a task to find the image that most accurately depicts the correct sense of the target word for the given context. Previously, image-text matching models often suffered from recognizing polysemous words. This
Externí odkaz:
http://arxiv.org/abs/2305.01788
Autor:
Kim, Yeji, Lee, Minhwa *
Publikováno v:
In Journal of Open Innovation: Technology, Market, and Complexity December 2019 5(4)
Autor:
Yun, Yeji, Lee, Minhwa *
Publikováno v:
In Journal of Open Innovation: Technology, Market, and Complexity December 2019 5(4)
Akademický článek
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Autor:
Lee, MinHwa, Yun, JinHyo Joseph *, Pyka, Andreas, Won, DongKyu, Kodama, Fumio, Schiuma, Giovanni, Park, HangSik, Jeon, Jeonghwan, Park, KyungBae, Jung, KwangHo, Yan, Min-Ren, Lee, SamYoul, Zhao, Xiaofei
Publikováno v:
In Journal of Open Innovation: Technology, Market, and Complexity September 2018 4(3)