Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Park, Choonghyun"'
Autor:
Kim, Youna, Kim, Hyuhng Joon, Park, Cheonbok, Park, Choonghyun, Cho, Hyunsoo, Kim, Junyeob, Yoo, Kang Min, Lee, Sang-goo, Kim, Taeuk
When using large language models (LLMs) in knowledge-intensive tasks, such as open-domain question answering, external context can bridge the gap between external knowledge and the LLMs' parametric knowledge. Recent research has been developed to amp
Externí odkaz:
http://arxiv.org/abs/2408.01084
Autor:
Park, Choonghyun, Kim, Hyuhng Joon, Kim, Junyeob, Kim, Youna, Kim, Taeuk, Cho, Hyunsoo, Jo, Hwiyeol, Lee, Sang-goo, Yoo, Kang Min
AI Generated Text (AIGT) detectors are developed with texts from humans and LLMs of common tasks. Despite the diversity of plausible prompt choices, these datasets are generally constructed with a limited number of prompts. The lack of prompt variati
Externí odkaz:
http://arxiv.org/abs/2406.16275
Autor:
Kim, Hyuhng Joon, Kim, Youna, Park, Cheonbok, Kim, Junyeob, Park, Choonghyun, Yoo, Kang Min, Lee, Sang-goo, Kim, Taeuk
In interactions between users and language model agents, user utterances frequently exhibit ellipsis (omission of words or phrases) or imprecision (lack of exactness) to prioritize efficiency. This can lead to varying interpretations of the same inpu
Externí odkaz:
http://arxiv.org/abs/2404.11972
Autor:
Kim, Hyuhng Joon, Cho, Hyunsoo, Lee, Sang-Woo, Kim, Junyeob, Park, Choonghyun, Lee, Sang-goo, Yoo, Kang Min, Kim, Taeuk
When deploying machine learning systems to the wild, it is highly desirable for them to effectively leverage prior knowledge to the unfamiliar domain while also firing alarms to anomalous inputs. In order to address these requirements, Universal Doma
Externí odkaz:
http://arxiv.org/abs/2310.14849
Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning
As the size of the pre-trained language model (PLM) continues to increase, numerous parameter-efficient transfer learning methods have been proposed recently to compensate for the tremendous cost of fine-tuning. Despite the impressive results achieve
Externí odkaz:
http://arxiv.org/abs/2301.11660
Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. Most recent studies in OOD detection utilize the information from a single
Externí odkaz:
http://arxiv.org/abs/2210.11034
Publikováno v:
AIP Conference Proceedings; 2006, Vol. 850 Issue 1, p1637-1638, 2p, 3 Graphs
