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Large Language Models (LLMs) have shown impressive abilities in many applications.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals
Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid O Séaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz. 2019 · 1911
Earlier work this paper cites.
Cours d’économie politique , volume 1
Vilfredo Pareto. 1964 · 1964
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al. 1999 · 1999
Earlier work this paper cites.
Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan. 2002 · 2002
Earlier work this paper cites.
Learning from rules generalizing labeled exemplars
Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, and Sunita Sarawagi. 2020 · 2004
Earlier work this paper cites.
Learning from crowds
Vikas C Raykar, Shipeng Yu, Linda H Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy. 2010 · 2010
Earlier work this paper cites.
Contributions to the study of sms spam filtering: new collection and results
Tiago A Almeida, José María G Hidalgo, and Akebo Yamakami. 2011 · 2011
Earlier work this paper cites.
Knowledge-based weak supervision for information extraction of overlapping relations
Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S Weld. 2011 · 2011
Earlier work this paper cites.
Multiple objective decision making—methods and applications: a state-of-the-art survey , volume 164
C-L Hwang and Abu Syed Md Masud. 2012 · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht. 2015 · 2015
Earlier work this paper cites.
Biocreative v cdr task corpus: a resource for chemical disease relation extraction
Jiao Li, Yueping Sun, Robin J Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J Mattingly, Thomas C Wiegers, and Zhiyong Lu. 2016 · 2016
Earlier work this paper cites.
Data programming: Creating large training sets, quickly
Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré. 2016 · 2016
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017 · 2017
Earlier work this paper cites.
Overview of the biocreative vi chemical-protein interaction track
Martin Krallinger, Obdulia Rabal, Saber A Akhondi, Martın Pérez Pérez, Jesús Santamaría, Gael Pérez Rodríguez, Georgios Tsatsaronis, Ander Intxaurrondo, José Antonio López, Umesh Nandal, et al. 2017 · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
Earlier work this paper cites.
Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2017 · 2017
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Deep probabilistic logic: A unifying framework for indirect supervision
Hai Wang and Hoifung Poon. 2018 · 2018
Cited alongside, same era.
Training complex models with multi-task weak supervision
Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré. 2019 · 2019
Cited alongside, same era.
Multi-resolution weak supervision for sequential data
Paroma Varma, Frederic Sala, Shiori Sagawa, Jason Fries, Daniel Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, et al. 2019 · 2019
Cited alongside, same era.
Fast and three-rious: Speeding up weak supervision with triplet methods
Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian, and Christopher Ré. 2020 · 2020
A survey on programmatic weak supervision
Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, and Alexander Ratner. 2022 · 2022
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Large language models and the perils of their hallucinations
Razvan Azamfirei, Sapna R Kudchadkar, and James Fackler. 2023 · 2023
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Benchmarking large language models in retrieval-augmented generation
Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2023 · 2023
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Evaluation of gpt-4 for 10-year cardiovascular risk prediction: insights from the uk biobank and koges data
Changho Han, Dong Won Kim, Songsoo Kim, Seng Chan You, Jin Young Park, SungA Bae, and Dukyong Yoon. 2023 · 2023
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2020 · 2020
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Nero: A neural rule grounding framework for label-efficient relation extraction
Wenxuan Zhou, Hongtao Lin, Bill Yuchen Lin, Ziqi Wang, Junyi Du, Leonardo Neves, and Xiang Ren. 2020 · 2020
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Comparative toxicogenomics database (ctd): update 2021
Allan Peter Davis, Cynthia J Grondin, Robin J Johnson, Daniela Sciaky, Jolene Wiegers, Thomas C Wiegers, and Carolyn J Mattingly. 2021 · 2021
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Self-supervised self-supervision by combining deep learning and probabilistic logic
Hunter Lang and Hoifung Poon. 2021 · 2021
Cited alongside, same era.
End-to-end weak supervision
Salva Rühling Cachay, Benedikt Boecking, and Artur Dubrawski. 2021 · 2021
Cited alongside, same era.
Sais: supervising and augmenting intermediate steps for document-level relation extraction
Yuxin Xiao, Zecheng Zhang, Yuning Mao, Carl Yang, and Jiawei Han. 2021 · 2021
Cited alongside, same era.
Fine-tuning pre-trained language model with weak supervision: A contrastive-regularized self-training approach
Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, and Chao Zhang. 2021 · 2021
Cited alongside, same era.
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Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Potsawee Manakul, Adian Liusie, and Mark JF Gales. 2023 · 2023
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Assessing the quality of multiple-choice questions using gpt-4 and rule-based methods
Steven Moore, Huy A Nguyen, Tianying Chen, and John Stamper. 2023 · 2023
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A brief report on lawgpt 1.0: A virtual legal assistant based on gpt-3
Ha-Thanh Nguyen. 2023 · 2023
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Capabilities of gpt-4 on medical challenge problems
Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, and Eric Horvitz. 2023 · 2023
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OpenAI. 2023 · 2023
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Use of gpt-4 to analyze medical records of patients with extensive investigations and delayed diagnosis
Yat-Fung Shea, Cynthia Min Yao Lee, Whitney Chin Tung Ip, Dik Wai Anderson Luk, and Stephanie Sze Wing Wong. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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Survey on factuality in large language models: Knowledge, retrieval and domain-specificity
Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, Yidong Wang, Linyi Yang, Jindong Wang, Xing Xie, Zheng Zhang, and Yue Zhang. 2023 · 2023
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Large language models are better reasoners with self-verification
Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, and Jun Zhao. 2023 · 2023
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A prompt pattern catalog to enhance prompt engineering with chatgpt
Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C Schmidt. 2023 · 2023
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Bloomberggpt: A large language model for finance
Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. 2023 · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023 · 2023
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