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Since the inception of the Transformer architecture in 2017, Large Language Models (LLMs) such as GPT and BERT have evolved significantly, impacting various industries with their advanced capabilities in language understanding and generation.
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2024
Closest in time.
I. Jahan, M. T. R. Laskar, C. Peng, and J. X. Huang, “A comprehensive evaluation of large language models on benchmark biomedical text processing tasks,” Computers in Biology and Medicine , p. 108189, 2024
2024
Closest in time.
E. Guo, M. Gupta, J. Deng, Y.-J. Park, M. Paget, and C. Naugler, “Automated paper screening for clinical reviews using large language models: Data analysis study,” Journal of Medical Internet Research , vol. 26, p. e48996, 2024
2024
Closest in time.
Y. Zhang, M. Lang, J. Jiang, Z. Gao, F. Xu, T. Litfin, K. Chen, J. Singh, X. Huang, G. Song et al. , “Multiple sequence alignment-based rna language model and its application to structural inference,” Nucleic Acids Research , vol. 52, no. 1, pp. e3–e3, 2024
2024
Closest in time.
2024
Closest in time.
Y. Cai, L. Wang, Y. Wang, G. de Melo, Y. Zhang, Y. Wang, and L. He, “Medbench: A large-scale chinese benchmark for evaluating medical large language models,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 16, 2024, pp. 17 709–17 717
2024
Closest in time.
2024
Closest in time.
A. Team, “Introducing the next generation of claude,” https://www.anthropic.com/news/claude-3-family , 2024
2024
Closest in time.
H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, Y. Li, X. Wang, M. Dehghani, S. Brahma et al. , “Scaling instruction-finetuned language models,” Journal of Machine Learning Research , vol. 25, no. 70, pp. 1–53, 2024
2024
Closest in time.