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pro vyhledávání: '"Kim, Jinseok P."'
Autor:
Lee, Janghwan, Park, Jiwoong, Kim, Jinseok, Kim, Yongjik, Oh, Jungju, Oh, Jinwook, Choi, Jungwook
Scaling Large Language Models (LLMs) with extended context lengths has increased the need for efficient low-bit quantization to manage their substantial computational demands. However, reducing precision to 4 bits frequently degrades performance due
Externí odkaz:
http://arxiv.org/abs/2411.09909
Despite the impressive capabilities of Large Language Models (LLMs) in various tasks, their vulnerability to unsafe prompts remains a critical issue. These prompts can lead LLMs to generate responses on illegal or sensitive topics, posing a significa
Externí odkaz:
http://arxiv.org/abs/2407.06851
Beyond Binary Gender Labels: Revealing Gender Biases in LLMs through Gender-Neutral Name Predictions
Autor:
You, Zhiwen, Lee, HaeJin, Mishra, Shubhanshu, Jeoung, Sullam, Mishra, Apratim, Kim, Jinseok, Diesner, Jana
Name-based gender prediction has traditionally categorized individuals as either female or male based on their names, using a binary classification system. That binary approach can be problematic in the cases of gender-neutral names that do not align
Externí odkaz:
http://arxiv.org/abs/2407.05271
Autor:
Kim, Jinseok, Kim, Tae-Kyun
Super-resolution (SR) and image generation are important tasks in computer vision and are widely adopted in real-world applications. Most existing methods, however, generate images only at fixed-scale magnification and suffer from over-smoothing and
Externí odkaz:
http://arxiv.org/abs/2403.10255
A fast and integrative algorithm for clustering performance evaluation in author name disambiguation
Autor:
Kim, Jinseok
Publikováno v:
Scientometrics, 120(2), 661-681 (2019)
Author name disambiguation results are often evaluated by measures such as Cluster-F, K-metric, Pairwise-F, Splitting & Lumping Error, and B-cubed. Although these measures have distinctive evaluation schemes, this paper shows that they can be calcula
Externí odkaz:
http://arxiv.org/abs/2102.03251
Autor:
Kim, Jinseok, Diesner, Jana
Publikováno v:
Social Network Analysis and Mining, 7(1), 1-12 (2017)
Applying the concept of triadic closure to coauthorship networks means that scholars are likely to publish a joint paper if they have previously coauthored with the same people. Prior research has identified moderate to high (20% to 40%) closure rate
Externí odkaz:
http://arxiv.org/abs/2102.03270
Generating automatically labeled data for author name disambiguation: An iterative clustering method
Publikováno v:
Scientometrics, 118(1), 253-280 (2019)
To train algorithms for supervised author name disambiguation, many studies have relied on hand-labeled truth data that are very laborious to generate. This paper shows that labeled training data can be automatically generated using information featu
Externí odkaz:
http://arxiv.org/abs/2102.03272
Autor:
Kim, Jinseok, Kim, Jenna
Publikováno v:
Journal of the Association for Information Science and Technology, 71(7), 839-855 (2020)
In author name disambiguation, author forenames are used to decide which name instances are disambiguated together and how much they are likely to refer to the same author. Despite such a crucial role of forenames, their effect on the performances of
Externí odkaz:
http://arxiv.org/abs/2102.03250
Autor:
Kim, Jinseok, Diesner, Jana
Publikováno v:
Scientometrics, 119(2), 687-706 (2019)
Link prediction in collaboration networks is often solved by identifying structural properties of existing nodes that are disconnected at one point in time, and that share a link later on. The maximally possible recall rate or upper bound of this app
Externí odkaz:
http://arxiv.org/abs/2102.03258
Autor:
Kim, Jinseok, Owen-Smith, Jason
How can we evaluate the performance of a disambiguation method implemented on big bibliographic data? This study suggests that the open researcher profile system, ORCID, can be used as an authority source to label name instances at scale. This study
Externí odkaz:
http://arxiv.org/abs/2102.03237