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pro vyhledávání: '"Kim, Yekyung"'
Existing metrics for evaluating the factuality of long-form text, such as FACTSCORE (Min et al., 2023) and SAFE (Wei et al., 2024), decompose an input text into "atomic claims" and verify each against a knowledge base like Wikipedia. These metrics ar
Externí odkaz:
http://arxiv.org/abs/2406.19276
Autor:
Kim, Yekyung, Chang, Yapei, Karpinska, Marzena, Garimella, Aparna, Manjunatha, Varun, Lo, Kyle, Goyal, Tanya, Iyyer, Mohit
Publikováno v:
1st Conference on Language Modeling (COLM 2024)
While long-context large language models (LLMs) can technically summarize book-length documents (>100K tokens), the length and complexity of the documents have so far prohibited evaluations of input-dependent aspects like faithfulness. In this paper,
Externí odkaz:
http://arxiv.org/abs/2404.01261
Safely navigating street intersections is a complex challenge for blind and low-vision individuals, as it requires a nuanced understanding of the surrounding context - a task heavily reliant on visual cues. Traditional methods for assisting in this d
Externí odkaz:
http://arxiv.org/abs/2402.06794
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information
The success of NLP systems often relies on the availability of large, high-quality datasets. However, not all samples in these datasets are equally valuable for learning, as some may be redundant or noisy. Several methods for characterizing datasets
Externí odkaz:
http://arxiv.org/abs/2305.19344
Autor:
Zhou, Haoran a, 1, Jeong, Min Ju b, 1, Do, Jung Jae c, d, Lee, Hyo Jae c, d, Oh, Oui Jin b, Kim, Yekyung a, Kim, Gisung e, Jung, Jae Woong c, d, Yang, JungYup e, f, ⁎, Noh, Jun Hong b, ⁎, Kang, Sung Ho a, ⁎
Publikováno v:
In Chemical Engineering Journal 1 November 2024 499
Despite the success of mixup in data augmentation, its applicability to natural language processing (NLP) tasks has been limited due to the discrete and variable-length nature of natural languages. Recent studies have thus relied on domain-specific h
Externí odkaz:
http://arxiv.org/abs/2112.13969
Autor:
Kim, Yekyung
Recently, several studies have investigated active learning (AL) for natural language processing tasks to alleviate data dependency. However, for query selection, most of these studies mainly rely on uncertainty-based sampling, which generally does n
Externí odkaz:
http://arxiv.org/abs/2011.13570
Akademický článek
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Publikováno v:
In Separation and Purification Technology 31 May 2017 179:381-392
Akademický článek
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