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Large language models (LLMs) have achieved unprecedented performance by leveraging vast pretraining corpora, yet their performance remains suboptimal in knowledge-intensive domains such as medicine and scientific research, where high factual precision is required.
A mathematical theory of communication
Claude E Shannon. 1948 · 1948
Earlier work this paper cites.
The monte carlo method
Nicholas Metropolis and Stanislaw Ulam. 1949 · 1949
Earlier work this paper cites.
Remarks on some nonparametric estimates of a density function
M Rosenblat. 1956 · 1956
Earlier work this paper cites.
On estimation of a probability density function and mode
Emanuel Parzen. 1962 · 1962
Earlier work this paper cites.
Multi-armed bandits with episode context
Christopher D Rosin. 2011 · 2011
Earlier work this paper cites.
Event extraction via dynamic multi-pooling convolutional neural networks
Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, and Jun Zhao. 2015 · 2015
Earlier work this paper cites.
Structural information and dynamical complexity of networks
Angsheng Li and Yicheng Pan. 2016 · 2016
Earlier work this paper cites.
Umap: Uniform manifold approximation and projection
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger. 2018 · 2018
Earlier work this paper cites.
Pubmedqa: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019 · 2019
Earlier work this paper cites.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020 · 2020
Earlier work this paper cites.
How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Earlier work this paper cites.
Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2021 · 2021
Earlier work this paper cites.
How can we know when language models know? on the calibration of language models for question answering
Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig. 2021 · 2021
Earlier work this paper cites.
What disease does this patient have? a large-scale open domain question answering dataset from medical exams
Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2021 · 2021
Earlier work this paper cites.
Logiqa: a challenge dataset for machine reading comprehension with logical reasoning
Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang. 2021 · 2021
Earlier work this paper cites.
Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. 2022 · 2022
Earlier work this paper cites.
Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini. 2022 · 2022
Cited alongside, same era.
Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering
Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Cited alongside, same era.
Star: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. 2022 · 2022
Cited alongside, same era.
Huatuogpt-ii, one-stage training for medical adaption of llms
Junying Chen, Xidong Wang, Ke Ji, Anningzhe Gao, Feng Jiang, Shunian Chen, Hongbo Zhang, Dingjie Song, Wenya Xie, Chuyi Kong, et al. 2023 · 2023
Cited alongside, same era.
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. 2024 · 2024
Later among the works it cites.
Human-instruction-free llm self-alignment with limited samples
Hongyi Guo, Yuanshun Yao, Wei Shen, Jiaheng Wei, Xiaoying Zhang, Zhaoran Wang, and Yang Liu. 2024 · 2024
Later among the works it cites.
Unfamiliar finetuning examples control how language models hallucinate
Katie Kang, Eric Wallace, Claire Tomlin, Aviral Kumar, and Sergey Levine. 2024 · 2024
Later among the works it cites.
Science of Artificial Intelligence: Mathematical Principles of Intelligence (In Chinese)
Angsheng Li. 2024 · 2024
Later among the works it cites.
Mathgenie: Generating synthetic data with question back-translation for enhancing mathematical reasoning of llms
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Medalpaca–an open-source collection of medical conversational ai models and training data
Tianyu Han, Lisa C Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, and Keno K Bressem. 2023 · 2023
Cited alongside, same era.
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Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
Cited alongside, same era.
The scalable precision medicine open knowledge engine (spoke): a massive knowledge graph of biomedical information
John H Morris, Karthik Soman, Rabia E Akbas, Xiaoyuan Zhou, Brett Smith, Elaine C Meng, Conrad C Huang, Gabriel Cerono, Gundolf Schenk, Angela Rizk-Jackson, et al. 2023 · 2023
Cited alongside, same era.
Synthetic data: The new fuel for ai
PwC Australia. 2023 · 2023
Cited alongside, same era.
Gpqa: A graduate-level google-proof q&a benchmark
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Cited alongside, same era.
Investigating the factual knowledge boundary of large language models with retrieval augmentation
Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hao Tian, Hua Wu, Ji-Rong Wen, and Haifeng Wang. 2023 · 2023
Cited alongside, same era.
Principle-driven self-alignment of language models from scratch with minimal human supervision
Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David Cox, Yiming Yang, and Chuang Gan. 2023 · 2023
Cited alongside, same era.
Zimu Lu, Aojun Zhou, Houxing Ren, Ke Wang, Weikang Shi, Junting Pan, Mingjie Zhan, and Hongsheng Li. 2024 · 2024
Later among the works it cites.
Biomedical knowledge graph-optimized prompt generation for large language models
Karthik Soman, Peter W Rose, John H Morris, Rabia E Akbas, Brett Smith, Braian Peetoom, Catalina Villouta-Reyes, Gabriel Cerono, Yongmei Shi, Angela Rizk-Jackson, et al. 2024 · 2024
Later among the works it cites.
Codeclm: Aligning language models with tailored synthetic data
Zifeng Wang, Chun-Liang Li, Vincent Perot, Long Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, and Tomas Pfister. 2024 · 2024
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Pmc-llama: toward building open-source language models for medicine
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Quiet-star: Language models can teach themselves to think before speaking
Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, and Noah D Goodman. 2024 · 2024
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