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researcher

Yun Zhao

UCSB, Meta

17 papers hereh-index 12630 citations24 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author5
  • middle author9
  • last author2

Across the 17 of 17 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.CL4
  • cs.CV2
  • cs.AI1
  • cs.CE1
  • eess.SP1
affiliations
  • UCSB, Meta
Homepage
same name
  • Yun Zhao — 10 papers, h 11
  • Yun Zhao — 10 papers, h 14
  • Yun Zhao — 5 papers, h 5
  • Yun Zhao — 4 papers, h 3
  • Yun Zhao — 3 papers
  • Yun Zhao — 3 papers, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192023
most citedAre Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding

29 citations · 69 across the 16 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2023★ 5 cited

TRAM: Benchmarking Temporal Reasoning for Large Language Models

Yuqing Wang, Yun Zhao

Reasoning about time is essential for understanding the nuances of events described in natural language. Previous research on this topic has been limited in scope, characterized by…

cs.CL2023★ 10 cited

Metacognitive Prompting Improves Understanding in Large Language Models

Yuqing Wang, Yun Zhao

In Large Language Models (LLMs), there have been consistent advancements in task-specific performance, largely influenced by effective prompt design. Recent advancements in prompti…

cs.CL2023★ 29 cited

Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding

Yuqing Wang, Yun Zhao, Linda Petzold

Large language models (LLMs) have made significant progress in various domains, including healthcare. However, the specialized nature of clinical language understanding tasks prese…

cs.CL2019

An Auxiliary Classifier Generative Adversarial Framework for Relation Extraction

Yun Zhao

Relation extraction models suffer from limited qualified training data. Using human annotators to label sentences is too expensive and does not scale well especially when dealing w…

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