Zobrazeno 1 - 10
of 55
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, Jeong, Min Ju, Do, Jung Jae, Lee, Hyo Jae, Oh, Oui Jin, Kim, Yekyung, Kim, Gisung, Jung, Jae Woong, Yang, JungYup, Noh, Jun Hong, Kang, Sung Ho
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
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
In Separation and Purification Technology 31 May 2017 179:381-392