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Large Language Models (LLMs) demonstrate remarkable versatility in various NLP tasks but encounter distinct challenges in biomedical due to the complexities of language and data scarcity.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Publicly available clinical bert embeddings
Emily Alsentzer, John R Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew McDermott. 2019 · 1904
Earlier work this paper cites.
Text chunking using transformation-based learning
Lance A Ramshaw and Mitchell P Marcus. 1999 · 1999
Earlier work this paper cites.
The unified medical language system (umls): integrating biomedical terminology
Olivier Bodenreider. 2004 · 2004
Earlier work this paper cites.
A framework for learning predictive structures from multiple tasks and unlabeled data
Rie Kubota Ando and Tong Zhang. 2005 · 2005
Earlier work this paper cites.
Scalable training of L1-regularized log-linear models
Galen Andrew and Jianfeng Gao. 2007 · 2007
Earlier work this paper cites.
Overview of biocreative ii gene mention recognition
Larry Smith, Lorraine K Tanabe, Rie Johnson nee Ando, Cheng-Ju Kuo, I-Fang Chung, Chun-Nan Hsu, Yu-Shi Lin, Roman Klinger, Christoph M Friedrich, Kuzman Ganchev, et al. 2008 · 2008
Earlier work this paper cites.
2010 i2b2/va challenge on concepts, assertions, and relations in clinical text
Özlem Uzuner, Brett R South, Shuying Shen, and Scott L DuVall. 2011 · 2010
Earlier work this paper cites.
Ncbi disease corpus: a resource for disease name recognition and concept normalization
Rezarta Islamaj Doğan, Robert Leaman, and Zhiyong Lu. 2014 · 2014
Earlier work this paper cites.
Yara parser: A fast and accurate dependency parser
Mohammad Sadegh Rasooli and Joel R. Tetreault. 2015 · 2015
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020 · 2020
Earlier work this paper cites.
Mpnet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2020 · 2020
Earlier work this paper cites.
Openprompt: An open-source framework for prompt-learning
Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Hai-Tao Zheng, and Maosong Sun. 2021 · 2021
Earlier work this paper cites.
Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
Earlier work this paper cites.
A deep database of medical abbreviations and acronyms for natural language processing
Lisa Grossman Liu, Raymond H Grossman, Elliot G Mitchell, Chunhua Weng, Karthik Natarajan, George Hripcsak, and David K Vawdrey. 2021 · 2021
Cited alongside, same era.
Degree: A data-efficient generation-based event extraction model
I Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng, et al. 2021 · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Why can gpt learn in-context? language models secretly perform gradient descent as meta-optimizers
Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Shuming Ma, Zhifang Sui, and Furu Wei. 2023 · 2023
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Gpt-3.5, gpt-4, or bard? evaluating llms reasoning ability in zero-shot setting and performance boosting through prompts
Jessica López Espejel, El Hassane Ettifouri, Mahaman Sanoussi Yahaya Alassan, El Mehdi Chouham, and Walid Dahhane. 2023 · 2023
Later among the works it cites.
What makes good in-context demonstrations for code intelligence tasks with llms?
Shuzheng Gao, Xin-Cheng Wen, Cuiyun Gao, Wenxuan Wang, Hongyu Zhang, and Michael R Lyu. 2023a · 2023
Later among the works it cites.
Yu Gu, Sheng Zhang, Naoto Usuyama, Yonas Woldesenbet, Cliff Wong, Praneeth Sanapathi, Mu Wei, Naveen Valluri, Erika Strandberg, Tristan Naumann, et al. 2023 · 2023
Later among the works it cites.
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Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2021 · 2021
Cited alongside, same era.
Gpt-3 models are poor few-shot learners in the biomedical domain
Milad Moradi, Kathrin Blagec, Florian Haberl, and Matthias Samwald. 2021 · 2021
Cited alongside, same era.
Structured prediction as translation between augmented natural languages
Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, Rishita Anubhai, Cicero Nogueira dos Santos, Bing Xiang, and Stefano Soatto. 2021 · 2021
Cited alongside, same era.
Hunflair: an easy-to-use tool for state-of-the-art biomedical named entity recognition
Leon Weber, Mario Sänger, Jannes Münchmeyer, Maryam Habibi, Ulf Leser, and Alan Akbik. 2021 · 2021
Cited alongside, same era.
Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick. 2021 · 2021
Cited alongside, same era.
A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022 · 2022
Cited alongside, same era.
Thinking about gpt-3 in-context learning for biomedical ie? think again
Bernal Jimenez Gutierrez, Nikolas McNeal, Clay Washington, You Chen, Lang Li, Huan Sun, and Yu Su. 2022 · 2022
Cited alongside, same era.
Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, and Robert McHardy. 2023 · 2023
Later among the works it cites.
Embracing large language models for medical applications: Opportunities and challenges
Mert Karabacak and Konstantinos Margetis. 2023 · 2023
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Large language models in hematology case solving: a comparative study of chatgpt-3.5, google bard, and microsoft bing
Amita Kumari, Anita Kumari, Amita Singh, Sanjeet K Singh, Ayesha Juhi, Anup Kumar D Dhanvijay, Mohammed Jaffer Pinjar, Himel Mondal, and Anoop Kumar Dhanvijay. 2023 · 2023
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Do we still need clinical language models?
Eric Lehman, Evan Hernandez, Diwakar Mahajan, Jonas Wulff, Micah J Smith, Zachary Ziegler, Daniel Nadler, Peter Szolovits, Alistair Johnson, and Emily Alsentzer. 2023 · 2023
Later among the works it cites.
Exploring the effectiveness of instruction tuning in biomedical language processing
Omid Rohanian, Mohammadmahdi Nouriborji, and David A Clifton. 2023 · 2023
Later among the works it cites.
Chatgpt: Is this version good for healthcare and research?
Raju Vaishya, Anoop Misra, and Abhishek Vaish. 2023 · 2023
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A prompt pattern catalog to enhance prompt engineering with chatgpt
Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C Schmidt. 2023 · 2023
Later among the works it cites.
Universalner: Targeted distillation from large language models for open named entity recognition
Wenxuan Zhou, Sheng Zhang, Yu Gu, Muhao Chen, and Hoifung Poon. 2023 · 2023
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Retrieval-augmented data augmentation for low-resource domain tasks
Minju Seo, Jinheon Baek, James Thorne, and Sung Ju Hwang. 2024 · 2024
Closest in time.
Opportunities and challenges for chatgpt and large language models in biomedicine and health
Shubo Tian, Qiao Jin, Lana Yeganova, Po-Ting Lai, Qingqing Zhu, Xiuying Chen, Yifan Yang, Qingyu Chen, Won Kim, Donald C Comeau, et al. 2024 · 2024
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Almanac—retrieval-augmented language models for clinical medicine
Cyril Zakka, Rohan Shad, Akash Chaurasia, Alex R Dalal, Jennifer L Kim, Michael Moor, Robyn Fong, Curran Phillips, Kevin Alexander, Euan Ashley, et al. 2024 · 2024
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