GCRE-GPT: A Generative Model for Comparative Relation Extraction
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
Given comparative text, comparative relation extraction aims to extract two targets (\eg two cameras) in comparison and the aspect they are compared for (\eg image quality). The extracted comparative relations form the basis of further opinion analysis.Existing solutions formulate this task as a sequence labeling task, to extract targets and aspects. However, they cannot directly extract comparative relation(s) from text. In this paper, we show that comparative relations can be directly extracted with high accuracy, by generative model. Based on GPT-2, we propose a Generation-based Comparative Relation Extractor (GCRE-GPT). Experiment results show that \modelname achieves state-of-the-art accuracy on two datasets.
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Seoul National University（首尔大学）；Ewha Womans University Medical Center（梨花女子大学医疗院）；Seoul National University Hospital and College of Medicine（首尔大学医院暨医学院）
Utilizing Large Language Models for Named Entity Recognition in Traditional Chinese Medicine against COVID-19 Literature: Comparative Study
China Academy of Chinese Medical Sciences（中国中医科学院）；GESIS—Leibniz Institute for the Social Sciences（GESIS——莱布尼茨社会科学研究所）；University of Bonn（波恩大学）；Capital Medical University（首都医科大学）
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Thornton Tomasetti, Inc.（Thornton Tomasetti 公司）；Yarmouk University（耶尔穆克大学）；Ministry of Municipal Affairs（市政事务部）；Jordan University of Science and Technology（约旦科技大学）
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Department of Information Sciences and Technology, George Mason University, Fairfax, VA, USA（信息科学与技术系，乔治·玛莎大学，弗吉尼亚州， Fairfax）；Department of Linguistics, University of Washington, Seattle, WA, USA（语言学系，华盛顿大学，西雅图，华盛顿州，美国）；Department of Radiology, School of Medicine, University of Washington, Seattle, WA, USA（放射学系，医学院，华盛顿大学，西雅图，华盛顿州，美国）
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Pepperdine University（佩珀代因大学）；Keck Data Science Institute（凯克数据科学研究所）；David Geffen School of Medicine, University of California, Los Angeles（加州大学洛杉矶分校大卫·格芬医学院）；Brain Research Institute, University of California, Los Angeles（加州大学洛杉矶分校脑研究所）
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GESIS – Leibniz Institute for the Social Sciences（GESIS社会科学院研究所）；RWTH Aachen University（亚琛RWTH大学）；University of Graz（格拉茨大学）；Complexity Science Hub（复杂性科学中心）
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Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院 Athinoula A. Martinos 生物医学成像中心）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Dana-Farber Cancer Institute（丹娜法伯癌症研究院）
Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Medical Faculty Heidelberg, Heidelberg University（海德堡大学医学院）；Bilkent University（比尔肯特大学）；Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital（海德堡大学医院放射肿瘤学系模式分析与学习组）
A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification
Bakar Computational Health Sciences Institute, University of California, San Francisco（加州大学旧金山分校巴卡尔计算健康科学研究所）；Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco（加州大学旧金山分校海伦·迪勒家族综合癌症中心）；University of California, Berkeley（加州大学伯克利分校）；Center for Data-driven Insights and Innovation, University of California, Office of the President（加州大学校长办公室数据驱动洞察与创新中心）；Department of Pediatrics, University of California, San Francisco（加州大学旧金山分校儿科系）
Evaluating Text Summaries Generated by Large Language Models Using OpenAI's GPT
University of Colorado - Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；University of Kansas（堪萨斯大学）；DataCrunch Lab, LLC（DataCrunch实验室有限责任公司）；University of Florida（佛罗里达大学）
