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pro vyhledávání: '"Won, Seungpil"'
Autor:
Kim, Jiyeon, Lee, Hyunji, Cho, Hyowon, Jang, Joel, Hwang, Hyeonbin, Won, Seungpil, Ahn, Youbin, Lee, Dohaeng, Seo, Minjoon
In this work, we investigate how a model's tendency to broadly integrate its parametric knowledge evolves throughout pretraining, and how this behavior affects overall performance, particularly in terms of knowledge acquisition and forgetting. We int
Externí odkaz:
http://arxiv.org/abs/2410.01380
Fact verification datasets are typically constructed using crowdsourcing techniques due to the lack of text sources with veracity labels. However, the crowdsourcing process often produces undesired biases in data that cause models to learn spurious p
Externí odkaz:
http://arxiv.org/abs/2109.15107
Applying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language. One line of research resolves this issue by employing reinforcement learning (RL) and optimizing the next-word sampling polic
Externí odkaz:
http://arxiv.org/abs/2010.08213
Autor:
Yoon, Seunghyun, Park, Kunwoo, Shin, Joongbo, Lim, Hongjun, Won, Seungpil, Cha, Meeyoung, Jung, Kyomin
Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume. This research introduces million-scale pairs of news headline and body tex
Externí odkaz:
http://arxiv.org/abs/1811.07066
Akademický článek
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