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Large Language Models (LLMs) have demonstrated remarkable success in diverse natural language processing (NLP) tasks in general domains.
Language models are few-shot learners
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Clinicalbert: Modeling clinical notes and predicting hospital readmission
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A coefficient of agreement for nominal scales
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The representation of meaning in the UMLS
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The probabilistic relevance framework: BM25 and beyond
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Preliminary study on the construction of Chinese medical knowledge graph
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Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets
Peng, Y.; Yan, S.; and Lu, Z. 2019 · 2019
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee, J.; Yoon, W.; Kim, S.; Kim, D.; Kim, S.; So, C. H.; and Kang, J. 2020 · 2020
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Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art
Lewis, P.; Ott, M.; Du, J.; and Stoyanov, V. 2020 · 2020
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Real-world data medical knowledge graph: construction and applications
Li, L.; Wang, P.; Yan, J.; Wang, Y.; Li, S.; Jiang, J.; Sun, Z.; Tang, B.; Chang, T.-H.; Wang, S.; et al. 2020 · 2020
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Domain-specific language model pretraining for biomedical natural language processing
Gu, Y.; Tinn, R.; Cheng, H.; Lucas, M.; Usuyama, N.; Liu, X.; Naumann, T.; Gao, J.; and Poon, H. 2021 · 2021
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LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W.; et al. 2021 · 2021
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UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus
Michalopoulos, G.; Wang, Y.; Kaka, H.; Chen, H.; and Wong, A. 2021 · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Nakano, R.; Hilton, J.; Balaji, S.; Wu, J.; Ouyang, L.; Kim, C.; Hesse, C.; Jain, S.; Kosaraju, V.; Saunders, W.; et al. 2021 · 2021
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SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining
Zhang, T.; Cai, Z.; Wang, C.; Qiu, M.; Yang, B.; and He, X. 2021 · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Bai, Y.; Jones, A.; Ndousse, K.; Askell, A.; Chen, A.; DasSarma, N.; Drain, D.; Fort, S.; Ganguli, D.; Henighan, T.; et al. 2022 · 2022
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Scaling instruction-finetuned language models
Chung, H. W.; Hou, L.; Longpre, S.; Zoph, B.; Tay, Y.; Fedus, W.; Li, E.; Wang, X.; Dehghani, M.; Brahma, S.; et al. 2022 · 2022
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A Survey on Evaluation of Large Language Models
Chang, Y.; Wang, X.; Wang, J.; Wu, Y.; Zhu, K.; Chen, H.; Yang, L.; Yi, X.; Wang, C.; Wang, Y.; et al. 2023 · 2023
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Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca
Cui, Y.; Yang, Z.; and Yao, X. 2023 · 2023
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Survey of hallucination in natural language generation
Ji, Z.; Lee, N.; Frieske, R.; Yu, T.; Su, D.; Xu, Y.; Ishii, E.; Bang, Y. J.; Madotto, A.; and Fung, P. 2023 · 2023
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ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge
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BioGPT: generative pre-trained transformer for biomedical text generation and mining
Luo, R.; Sun, L.; Xia, Y.; Qin, T.; Zhang, S.; Poon, H.; and Liu, T.-Y. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; et al. 2022 · 2022
Cited alongside, same era.
Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V.; Webson, A.; Raffel, C.; Bach, S. H.; Sutawika, L.; Alyafeai, Z.; Chaffin, A.; Stiegler, A.; Le Scao, T.; Raja, A.; et al. 2022 · 2022
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Bloom: A 176b-parameter open-access multilingual language model
Scao, T. L.; Fan, A.; Akiki, C.; Pavlick, E.; Ilić, S.; Hesslow, D.; Castagné, R.; Luccioni, A. S.; Yvon, F.; Gallé, M.; et al. 2022 · 2022
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Lamda: Language models for dialog applications
Thoppilan, R.; De Freitas, D.; Hall, J.; Shazeer, N.; Kulshreshtha, A.; Cheng, H.-T.; Jin, A.; Bos, T.; Baker, L.; Du, Y.; et al. 2022 · 2022
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Finetuned Language Models are Zero-Shot Learners
Wei, J.; Bosma, M.; Zhao, V.; Guu, K.; Yu, A. W.; Lester, B.; Du, N.; Dai, A. M.; and Le, Q. V. 2022 · 2022
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React: Synergizing reasoning and acting in language models
Yao, S.; Zhao, J.; Yu, D.; Du, N.; Shafran, I.; Narasimhan, K.; and Cao, Y. 2022 · 2022
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Dense text retrieval based on pretrained language models: A survey
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Mialon, G.; Dessì, R.; Lomeli, M.; Nalmpantis, C.; Pasunuru, R.; Raileanu, R.; Rozière, B.; Schick, T.; Dwivedi-Yu, J.; Celikyilmaz, A.; et al. 2023 · 2023
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ART: Automatic multi-step reasoning and tool-use for large language models
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Toolformer: Language models can teach themselves to use tools
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Stanford Alpaca: An Instruction-following LLaMA model
Taori, R.; Gulrajani, I.; Zhang, T.; Dubois, Y.; Li, X.; Guestrin, C.; Liang, P.; and Hashimoto, T. B. 2023 · 2023
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Llama: Open and efficient foundation language models
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HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge
Wang, H.; Liu, C.; Xi, N.; Qiang, Z.; Zhao, S.; Qin, B.; and Liu, T. 2023 · 2023
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Xiong, H.; Wang, S.; Zhu, Y.; Zhao, Z.; Liu, Y.; Wang, Q.; and Shen, D. 2023 · 2023
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HuatuoGPT, Towards Taming Language Models To Be a Doctor
Zhang, H.; Chen, J.; Jiang, F.; Yu, F.; Chen, Z.; Li, J.; Chen, G.; Wu, X.; Zhang, Z.; Xiao, Q.; Wan, X.; Wang, B.; and Li, H. 2023 · 2023
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