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Guiding large language models with a selected set of human-authored demonstrations is a common practice for improving LLM applications.
Language models are few-shot learners
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Sentence-bert: Sentence embeddings using siamese bert-networks
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Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification
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Education for judgment: The artistry of discussion leadership
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Teaching learners to be self-directed
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Research in clinical reasoning: past history and current trends
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The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
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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
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Measuring massive multitask language understanding
Hendrycks, D.; Burns, C.; Basart, S.; Zou, A.; Mazeika, M.; Song, D.; and Steinhardt, J. 2020 · 2009
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Multi-Layer Multi-View Classification for Alzheimer’s Disease Diagnosis
Zhang, C.; Adeli, E.; Zhou, T.; Chen, X.; and Shen, D. 2018 · 2018
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Introducing transfer learning to 3D ResNet-18 for Alzheimer’s disease detection on MRI images
Ebrahimi, A.; Luo, S.; and Chiong, R. 2020 · 2020
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Geva, M.; Khashabi, D.; Segal, E.; Khot, T.; Roth, D.; and Berant, J. 2021 · 2021
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What disease does this patient have? a large-scale open domain question answering dataset from medical exams
Jin, D.; Pan, E.; Oufattole, N.; Weng, W.-H.; Fang, H.; and Szolovits, P. 2021 · 2021
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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
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M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer
Jang, J.; and Hwang, D. 2022 · 2022
Cited alongside, same era.
Kim, H. J.; Cho, H.; Kim, J.; Kim, T.; Yoo, K. M.; and Lee, S.-g. 2022 · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Kojima, T.; Gu, S. S.; Reid, M.; Matsuo, Y.; and Iwasawa, Y. 2022 · 2022
Cited alongside, same era.
Learn to explain: Multimodal reasoning via thought chains for science question answering
Lu, P.; Mishra, S.; Xia, T.; Qiu, L.; Chang, K.-W.; Zhu, S.-C.; Tafjord, O.; Clark, P.; and Kalyan, A. 2022 · 2022
Cited alongside, same era.
Z-ICL: zero-shot in-context learning with pseudo-demonstrations
Lyu, X.; Min, S.; Beltagy, I.; Zettlemoyer, L.; and Hajishirzi, H. 2022 · 2022
Chiang, Y.; Chou, C.-H.; and Riebesell, J. 2024 · 2024
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Hwang, H.; Kim, D.; Kim, S.; Ye, S.; and Seo, M. 2024 · 2024
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Better zero-shot reasoning with role-play prompting
Kong, A.; Zhao, S.; Chen, H.; Li, Q.; Qin, Y.; Sun, R.; and Zhou, X. 2024 · 2024
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Large Language Models Are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales
Kwon, T.; Ong, K. T.-i.; Kang, D.; Moon, S.; Lee, J. R.; Hwang, D.; Sohn, B.; Sim, Y.; Lee, D.; and Yeo, J. 2024 · 2024
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Biomistral: A collection of open-source pretrained large language models for medical domains
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Cited alongside, same era.
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Min, S.; Lyu, X.; Holtzman, A.; Artetxe, M.; Lewis, M.; Hajishirzi, H.; and Zettlemoyer, L. 2022 · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Automatic chain of thought prompting in large language models
Zhang, Z.; Zhang, A.; Li, M.; and Smola, A. 2022 · 2022
Cited alongside, same era.
Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations
Chen, W.-L.; Wu, C.-K.; Chen, Y.-N.; and Chen, H.-H. 2023 · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Large language models encode clinical knowledge
Singhal, K.; Azizi, S.; Tu, T.; Mahdavi, S. S.; Wei, J.; Chung, H. W.; Scales, N.; Tanwani, A.; Cole-Lewis, H.; Pfohl, S.; et al. 2023 · 2023
Cited alongside, same era.
Better Zero-Shot Reasoning with Self-Adaptive Prompting
Wan, X.; Sun, R.; Dai, H.; Arik, S.; and Pfister, T. 2023 · 2023
Cited alongside, same era.
Labrak, Y.; Bazoge, A.; Morin, E.; Gourraud, P.-A.; Rouvier, M.; and Dufour, R. 2024 · 2024
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Self-Prompting Large Language Models for Zero-Shot Open-Domain QA
Li, J.; Wang, J.; Zhang, Z.; and Zhao, H. 2024a · 2024
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Let’s Verify Step by Step
Lightman, H.; Kosaraju, V.; Burda, Y.; Edwards, H.; Baker, B.; Lee, T.; Leike, J.; Schulman, J.; Sutskever, I.; and Cobbe, K. 2024 · 2024
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Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering
Liu, H.; Yin, H.; Luo, Z.; and Wang, X. 2024 · 2024
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Wang, X.; Wang, Y.; Zhang, Y.; Luo, F.; Li, P.; Sun, M.; and Liu, Y. 2024 · 2024
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Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs
Xiong, M.; Hu, Z.; Lu, X.; LI, Y.; Fu, J.; He, J.; and Hooi, B. 2024 · 2024
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Large Language Models as Optimizers
Yang, C.; Wang, X.; Lu, Y.; Liu, H.; Le, Q. V.; Zhou, D.; and Chen, X. 2024 · 2024
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MaScQA: investigating materials science knowledge of large language models
Zaki, M.; Krishnan, N. A.; et al. 2024 · 2024
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Self-discover: Large language models self-compose reasoning structures
Zhou, P.; Pujara, J.; Ren, X.; Chen, X.; Cheng, H.-T.; Le, Q. V.; Chi, E. H.; Zhou, D.; Mishra, S.; and Zheng, H. S. 2024 · 2024
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