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Closing the gap between open-source and commercial large language models
  for medical evidence summarization

Closing the gap between open-source and commercial large language models for medical evidence summarization

25 July 2024
Gongbo Zhang
Qiao Jin
Yiliang Zhou
Song Wang
B. Idnay
Yiming Luo
Elizabeth Park
Jordan G Nestor
Matthew E Spotnitz
Ali Soroush
Thomas Campion
Zhiyong Lu
Chunhua Weng
Yifan Peng
    ELMLM&MA
ArXiv (abs)PDFHTML

Papers citing "Closing the gap between open-source and commercial large language models for medical evidence summarization"

4 / 4 papers shown
Title
Position: Federated Foundation Language Model Post-Training Should Focus on Open-Source Models
Position: Federated Foundation Language Model Post-Training Should Focus on Open-Source Models
Nikita Agrawal
Simon Mertel
R. Mayer
95
0
0
29 May 2025
Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Large
Van-Tinh Nguyen
Hoang-Duong Pham
Thanh-Hai To
Cong-Tuan Hung Do
Thi-Thu-Trang Dong
Vu-Trung Duong Le
Van-Phuc Hoang
39
0
0
17 May 2025
Prompt-based Depth Pruning of Large Language Models
Prompt-based Depth Pruning of Large Language Models
Juyun Wee
Minjae Park
Jaeho Lee
VLM
199
0
0
04 Feb 2025
The Potential of LLMs in Medical Education: Generating Questions and Answers for Qualification Exams
The Potential of LLMs in Medical Education: Generating Questions and Answers for Qualification Exams
Yunqi Zhu
Wen Tang
Ying Sun
Xuebing Yang
Liyang Dou
Yifan Gu
Yuanyuan Wu
Wensheng Zhang
Ying Sun
Xuebing Yang
LM&MAELM
181
1
0
31 Oct 2024
1