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Scholarly Works (2 results)

Thesis
Peer Reviewed

Toward a New Paradigm of East Asian Yogācāra Buddhism: Taehyŏn (ca. 8th century CE), a Korean Yogācāra monk, and His Predecessors

Lee, Sumi
Advisor(s): Buswell, Robert E.

UCLA Electronic Theses and Dissertations (2014)

This dissertation seeks to locate the place of Taehyon (ca. 8th century CE), a Silla Korean Yogacara monk, within the broader East Asian Buddhist tradition. My task is not confined solely to a narrow study of Taehyon's thought and career, but is principally concerned with understanding the wider contours of the East Asian Yogacara tradition itself and how these contours are reflected in Taehyon's extant oeuvre. There are problems in determining Taehyon's doctrinal position within the traditional paradigms of East Asian Yogacara tradition, that is, the bifurcations of Tathagatagarbha and Yogacara; Old and New Yogacara; the One Vehicle and Three Vehicles; and the Dharma Nature and Dharma Characteristics schools. Taehyon's extant works contain doctrines drawn from across these various divides, and his doctrinal positions therefore do not precisely fit any of these traditional paradigms. In order to address this issue, this dissertation examines how these bifurcations originated and evolved over time, across the geographical expanse of the East Asian Yogacara tradition. The chapters of the dissertation discuss in largely chronological order the theoretical problems involved in these bifurcations within Yogacara and proposes possible resolutions to these problems, by focusing on the works of such major Buddhist exegetes as Paramartha (499-569), Ji (632-682), Wonhyo (617-686), Fazang (643-712), and, finally, Taehyon.

Cover page: Toward a New Paradigm of East Asian Yogācāra Buddhism: Taehyŏn (ca. 8th century CE), a Korean Yogācāra monk, and His Predecessors

Article
Peer Reviewed

Crowdsourcing with Enhanced Data Quality Assurance: An Efficient Approach to Mitigate Resource Scarcity Challenges in Training Large Language Models for Healthcare.

UC San Diego Previously Published Works (2024)

Large Language Models (LLMs) have demonstrated immense potential in artificial intelligence across various domains, including healthcare. However, their efficacy is hindered by the need for high-quality labeled data, which is often expensive and time-consuming to create, particularly in low-resource domains like healthcare. To address these challenges, we propose a crowdsourcing (CS) framework enriched with quality control measures at the pre-, real-time-, and post-data gathering stages. Our study evaluated the effectiveness of enhancing data quality through its impact on LLMs (Bio-BERT) for predicting autism-related symptoms. The results show that real-time quality control improves data quality by 19% compared to pre-quality control. Fine-tuning Bio-BERT using crowdsourced data generally increased recall compared to the Bio-BERT baseline but lowered precision. Our findings highlighted the potential of crowdsourcing and quality control in resource-constrained environments and offered insights into optimizing healthcare LLMs for informed decision-making and improved patient care.