Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Hong, Seokhee"'
Autor:
Research, LG AI, An, Soyoung, Bae, Kyunghoon, Choi, Eunbi, Choi, Stanley Jungkyu, Choi, Yemuk, Hong, Seokhee, Hong, Yeonjung, Hwang, Junwon, Jeon, Hyojin, Jo, Gerrard Jeongwon, Jo, Hyunjik, Jung, Jiyeon, Jung, Yountae, Kim, Euisoon, Kim, Hyosang, Kim, Joonkee, Kim, Seonghwan, Kim, Soyeon, Kim, Sunkyoung, Kim, Yireun, Kim, Youchul, Lee, Edward Hwayoung, Lee, Haeju, Lee, Honglak, Lee, Jinsik, Lee, Kyungmin, Lee, Moontae, Lee, Seungjun, Lim, Woohyung, Park, Sangha, Park, Sooyoun, Park, Yongmin, Seo, Boseong, Yang, Sihoon, Yeen, Heuiyeen, Yoo, Kyungjae, Yun, Hyeongu
We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model sizes, we publicly release the 7.8B instruction-tuned model to promote ope
Externí odkaz:
http://arxiv.org/abs/2408.03541
Autor:
Gray, Kathryn, Bell, Brian, Sieper, Diana, Kobourov, Stephen, Schreiber, Falk, Klein, Karsten, Hong, Seokhee
This paper aims to develop a mathematical foundation to model knitting with graphs. We provide a precise definition for knit objects with a knot theoretic component and propose a simple undirected graph, a simple directed graph, and a directed multig
Externí odkaz:
http://arxiv.org/abs/2407.00511
Large language models (LLMs) learn not only natural text generation abilities but also social biases against different demographic groups from real-world data. This poses a critical risk when deploying LLM-based applications. Existing research and re
Externí odkaz:
http://arxiv.org/abs/2305.17701
Autor:
Lee, Hwaran, Hong, Seokhee, Park, Joonsuk, Kim, Takyoung, Cha, Meeyoung, Choi, Yejin, Kim, Byoung Pil, Kim, Gunhee, Lee, Eun-Ju, Lim, Yong, Oh, Alice, Park, Sangchul, Ha, Jung-Woo
The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising. Existing works focus on coping with this concern while interacting with ill-intentioned users, such as those
Externí odkaz:
http://arxiv.org/abs/2305.17696
Autor:
Lee, Taehyun, Hong, Seokhee, Ahn, Jaewoo, Hong, Ilgee, Lee, Hwaran, Yun, Sangdoo, Shin, Jamin, Kim, Gunhee
Since the remarkable generation performance of large language models raised ethical and legal concerns, approaches to detect machine-generated text by embedding watermarks are being developed. However, we discover that the existing works fail to func
Externí odkaz:
http://arxiv.org/abs/2305.15060
Autor:
Bekos, Michael A., Cornelsen, Sabine, Fink, Martin, Hong, Seokhee, Kaufmann, Michael, Nöllenburg, Martin, Rutter, Ignaz, Symvonis, Antonios
In this paper we study \emph{many-to-one boundary labeling with backbone leaders}. In this new many-to-one model, a horizontal backbone reaches out of each label into the feature-enclosing rectangle. Feature points that need to be connected to this l
Externí odkaz:
http://arxiv.org/abs/1308.6801
Autor:
Liu, Shixia, Andrienko, Gennady, Wu, Yingcai, Cao, Nan, Jiang, Liu, Shi, Conglei, Wang, Yu-Shuen, Hong, Seokhee
Publikováno v:
In Visual Informatics December 2018 2(4):191-197
Publikováno v:
International Journal of Data Science & Analytics; Mar2023, Vol. 15 Issue 2, p159-171, 13p
Publikováno v:
International Journal of Data Science and Analytics; 20220101, Issue: Preprints p1-13, 13p
Autor:
Liu, Shixia, Andrienko, Gennady, Wu, Yingcai, Cao, Nan, Jiang, Liu, Shi, Conglei, Wang, Yu-Shuen, Hong, Seokhee
Publikováno v:
Visual Informatics; 20240101, Issue: Preprints