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Machine reasoning has made great progress in recent years owing to large language models (LLMs).
How Additional Knowledge can Improve Natural Language Commonsense Question Answering?
Mitra, A.; Banerjee, P.; Pal, K. K.; Mishra, S.; and Baral, C. 2020 · 1909
Earlier work this paper cites.
Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification
Qiu, S.; Joshi, P. S.; Miller, M. I.; Xue, C.; Zhou, X.; Karjadi, C.; Chang, G. H.; Joshi, A. S.; Dwyer, B.; Zhu, S.; et al. 2020 · 1933
Earlier work this paper cites.
Diagnostic reasoning
Kassirer, J. P. 1989 · 1989
Earlier work this paper cites.
Research in clinical reasoning: past history and current trends
Norman, G. 2005 · 2005
Earlier work this paper cites.
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack Jr, C. R.; Bernstein, M. A.; Fox, N. C.; Thompson, P.; Alexander, G.; Harvey, D.; Borowski, B.; Britson, P. J.; L. Whitwell, J.; Ward, C.; et al. 2008 · 2008
Earlier work this paper cites.
The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease
Ellis, K. A.; Bush, A. I.; Darby, D.; De Fazio, D.; Foster, J.; Hudson, P.; Lautenschlager, N. T.; Lenzo, N.; Martins, R. N.; Maruff, P.; et al. 2009 · 2009
Earlier work this paper cites.
New Criteria for Alzheimer’s disease and Mild Cognitive Impairment: Implications for the Practicing Clinician
Budson, A. E.; and Solomon, P. R. 2012 · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
Earlier work this paper cites.
Improving diagnosis in health care
Balogh, E. P.; Miller, B. T.; and Ball, J. R. 2015 · 2015
Earlier work this paper cites.
Distilling the Knowledge in a Neural Network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
Earlier work this paper cites.
Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis
Zhu, X.; Suk, H.-I.; Lee, S.-W.; and Shen, D. 2016 · 2016
Earlier work this paper cites.
Learning spatio-temporal features with 3d residual networks for action recognition
Hara, K.; Kataoka, H.; and Satoh, Y. 2017 · 2017
Earlier work this paper cites.
What do we need to build explainable AI systems for the medical domain?
Holzinger, A.; Biemann, C.; Pattichis, C. S.; and Kell, D. B. 2017 · 2017
Earlier work this paper cites.
A closer look at the hippocampus and memory
Voss, J. L.; Bridge, D. J.; Cohen, N. J.; and Walker, J. A. 2017 · 2017
Earlier work this paper cites.
Deep learning and medical diagnosis: A review of literature
Bakator, M.; and Radosav, D. 2018 · 2018
Earlier work this paper cites.
Management reasoning: beyond the diagnosis
Cook, D. A.; Sherbino, J.; and Durning, S. J. 2018 · 2018
Cited alongside, same era.
Current understanding of Alzheimer’s disease diagnosis and treatment
Weller, J.; and Budson, A. 2018 · 2018
Cited alongside, same era.
Multi-Layer Multi-View Classification for Alzheimer’s Disease Diagnosis
Zhang, C.; Adeli, E.; Zhou, T.; Chen, X.; and Shen, D. 2018 · 2018
Cited alongside, same era.
The neuropathological diagnosis of Alzheimer’s disease
DeTure, M. A.; and Dickson, D. W. 2019 · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Kenton, J. D. M.-W. C.; and Toutanova, L. K. 2019 · 2019
Cited alongside, same era.
Introducing transfer learning to 3D ResNet-18 for Alzheimer’s disease detection on MRI images
Ebrahimi, A.; Luo, S.; and Chiong, R. 2020 · 2020
Chain-of-thought prompting elicits reasoning in large language models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Xia, F.; Chi, E.; Le, Q. V.; Zhou, D.; et al. 2022 · 2022
Later among the works it cites.
Star: Bootstrapping reasoning with reasoning
Zelikman, E.; Wu, Y.; Mu, J.; and Goodman, N. 2022 · 2022
Later among the works it cites.
OPT: Open Pre-trained Transformer Language Models
Zhang, S.; Roller, S.; Goyal, N.; Artetxe, M.; Chen, M.; Chen, S.; Dewan, C.; Diab, M.; Li, X.; Lin, X. V.; Mihaylov, T.; Ott, M.; Shleifer, S.; Shuster, K.; Simig, D.; Koura, P. S.; Sridhar, A.; Wang, T.; and Zettlemoyer, L. 2022 · 2022
Later among the works it cites.
Alpa: Automating inter- and intra-operator parallelism for distributed deep learning
Zheng, L.; Li, Z.; Zhang, H.; Zhuang, Y.; Chen, Z.; Huang, Y.; Wang, Y.; Xu, Y.; Zhuo, D.; Xing, E. P.; et al. 2022 · 2022
Later among the works it cites.
CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models
Feng, S. Y.; Khetan, V.; Sacaleanu, B.; Gershman, A.; and Hovy, E. 2023 · 2023
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Cited alongside, same era.
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
Cited alongside, same era.
Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset
Yue, X.; Jimenez Gutierrez, B.; and Sun, H. 2020 · 2020
Cited alongside, same era.
Cross-domain reasoning via template filling
Rajagopal, D.; Khetan, V.; Sacaleanu, B.; Gershman, A.; Fano, A.; and Hovy, E. 2021 · 2021
Cited alongside, same era.
Multimodal few-shot learning with frozen language models
Tsimpoukelli, M.; Menick, J. L.; Cabi, S.; Eslami, S.; Vinyals, O.; and Hill, F. 2021 · 2021
Cited alongside, same era.
Dual attention multi-instance deep learning for Alzheimer’s disease diagnosis with structural MRI
Zhu, W.; Sun, L.; Huang, J.; Han, L.; and Zhang, D. 2021 · 2021
Cited alongside, same era.
Large language models are few-shot clinical information extractors
Agrawal, M.; Hegselmann, S.; Lang, H.; Kim, Y.; and Sontag, D. 2022 · 2022
Cited alongside, same era.
Closest in time.
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Hsieh, C.-Y.; Li, C.-L.; Yeh, C.-k.; Nakhost, H.; Fujii, Y.; Ratner, A.; Krishna, R.; Lee, C.-Y.; and Pfister, T. 2023 · 2023
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Natural language processing to predict isocitrate dehydrogenase genotype in diffuse glioma using MR radiology reports
Kim, M.; Ong, K. T.-i.; Choi, S.; Yeo, J.; Kim, S.; Han, K.; Park, J. E.; Kim, H. S.; Choi, Y. S.; Ahn, S. S.; et al. 2023 · 2023
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CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models
Li, T.; Shetty, S.; Kamath, A.; Jaiswal, A.; Jiang, X.; Ding, Y.; and Kim, Y. 2023 · 2023
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Can large language models reason about medical questions?
Liévin, V.; Hother, C. E.; and Winther, O. 2023 · 2023
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Lost in the Middle: How Language Models Use Long Contexts
Liu, N. F.; Lin, K.; Hewitt, J.; Paranjape, A.; Bevilacqua, M.; Petroni, F.; and Liang, P. 2023 · 2023
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Evidence-empowered Transfer Learning for Alzheimer’s Disease
Ong, K. T.-i.; Kim, H.; Kim, M.; Jang, J.; Sohn, B.; Choi, Y. S.; Hwang, D.; Hwang, S. J.; and Yeo, J. 2023 · 2023
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Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; et al. 2023 · 2023
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SCOTT: Self-Consistent Chain-of-Thought Distillation
Wang, P.; Wang, Z.; Li, Z.; Gao, Y.; Yin, B.; and Ren, X. 2023 · 2023
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Chain of Thought Prompting Elicits Knowledge Augmentation
Wu, D.; Zhang, J.; and Huang, X. 2023 · 2023
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Multimodal Chain-of-Thought Reasoning in Language Models
Zhang, Z.; Zhang, A.; Li, M.; Zhao, H.; Karypis, G.; and Smola, A. 2023 · 2023
Closest in time.