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A popular approach for improving the correctness of output from large language models (LLMs) is Self-Consistency - poll the LLM multiple times and output the most frequent solution.
Reducing transformer depth on demand with structured dropout
Angela Fan, Edouard Grave, and Armand Joulin. 2019 · 1909
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Maximum likelihood estimation of observer error-rates using the em algorithm
A. Philip Dawid and Allan Skene. 1979 · 1979
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Hyperparameter estimation in dirichlet process mixture models
Beate West. 1992 · 1992
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Dynabert: Dynamic bert with adaptive width and depth
Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, and Qun Liu. 2020 · 2004
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Adaptive importance sampling to accelerate training of a neural probabilistic language model
Yoshua Bengio and Jean-Sébastien Senecal. 2008 · 2008
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Whose vote should count more: Optimal integration of labels from labelers of unknown expertise
Jacob Whitehill, Paul Ruvolo, Tingfan Wu, Jacob Bergsma, and Javier R. Movellan. 2009 · 2009
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Learning from crowds
Vikas Chandrakant Raykar, Shipeng Yu, Linda H. Zhao, Gerardo Hermosillo, Charles Florin, Luca Bogoni, and Linda Moy. 2010 · 2010
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The multidimensional wisdom of crowds
Peter Welinder, Steve Branson, Serge J. Belongie, and Pietro Perona. 2010 · 2010
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Crowdsourcing systems on the world-wide web
AnHai Doan, Raghu Ramakrishnan, and Alon Y. Halevy. 2011 · 2011
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Human computation: a survey and taxonomy of a growing field
Alexander J. Quinn and Benjamin B. Bederson. 2011 · 2011
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Crowdsourcing control: Moving beyond multiple choice
Christopher H. Lin, Mausam, and Daniel S. Weld. 2012 · 2012
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Pomdp-based control of workflows for crowdsourcing
Peng Dai, C. H. Lin, Mausam, and Daniel S. Weld. 2013 · 2013
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Artificial intelligence and collective intelligence
Daniel S Weld, Mausam, Christopher H Lin, and Jonathan Bragg. 2015 · 2015
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Optimal testing for crowd workers
Jonathan Bragg, Mausam, and Daniel S. Weld. 2016 · 2016
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An adaptive sampling scheme to efficiently train fully convolutional networks for semantic segmentation
Lorenz Berger, Eoin R. Hyde, M. Jorge Cardoso, and Sébastien Ourselin. 2017 · 2017
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Sampling bias in deep active classification: An empirical study
Ameya Prabhu, Charles Dognin, and Maneesh Kumar Singh. 2019 · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Cited alongside, same era.
Fastbert: a self-distilling bert with adaptive inference time
Weijie Liu, Peng Zhou, Zhiruo Wang, Zhe Zhao, Haotang Deng, and Qi Ju. 2020 · 2020
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A diverse corpus for evaluating and developing English math word problem solvers
Shen-yun Miao, Chao-Chun Liang, and Keh-Yih Su. 2020 · 2020
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Deebert: Dynamic early exiting for accelerating bert inference
Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, and Jimmy J. Lin. 2020 · 2020
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Early exit or not: Resource-efficient blind quality enhancement for compressed images
Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2022 · 2022
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Large Language Models are Zero-Shot Reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Solving quantitative reasoning problems with language models
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, et al. 2022 · 2022
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Competition-level code generation with alphacode
Yujia Li, David H. Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom, Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de, Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey, Cherepanov, James Molloy, Daniel Jaymin Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de, Freitas, Koray Kavukcuoglu, and Oriol Vinyals. 2022 · 2022
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Qunliang Xing, Mai Xu, Tianyi Li, and Zhenyu Guan. 2020 · 2020
Cited alongside, same era.
Bert loses patience: Fast and robust inference with early exit
Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, and Furu Wei. 2020 · 2020
Cited alongside, same era.
Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, and Charles Sutton. 2021 · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harrison Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, David W. Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William H. Guss, Alex Nichol, Igor Babuschkin, S. Arun Balaji, Shantanu Jain, Andrew Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew M. Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021 · 2021
Cited alongside, same era.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
Cited alongside, same era.
Romebert: Robust training of multi-exit bert
Shijie Geng, Peng Gao, Zuohui Fu, and Yongfeng Zhang. 2021 · 2021
Cited alongside, same era.
Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021 · 2021
Cited alongside, same era.
Measuring coding challenge competence with apps
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Xiaodong Song, and Jacob Steinhardt. 2021 · 2021
Cited alongside, same era.
Flowgen: Fast and slow graph generation
Aman Madaan and Yiming Yang. 2022 · 2022
Later among the works it cites.
Bhavana Dalvi Mishra, Oyvind Tafjord, and Peter Clark. 2022 · 2022
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Confident adaptive language modeling
Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Q Tran, Yi Tay, and Donald Metzler. 2022 · 2022
Later among the works it cites.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Huai hsin Chi, F. Xia, Quoc Le, and Denny Zhou. 2022 · 2022
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Have llms advanced enough? a challenging problem solving benchmark for large language models
Daman Arora, Himanshu Gaurav Singh, and Mausam. 2023 · 2023
Closest in time.
Frugalgpt: How to use large language models while reducing cost and improving performance
Lingjiao Chen, Matei A. Zaharia, and James Y. Zou. 2023 · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023 · 2023
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Revisiting prompt engineering via declarative crowdsourcing
Aditya G. Parameswaran, Shreya Shankar, Parth Asawa, Naman Jain, and Yujie Wang. 2023 · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 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, Aur’elien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Adaptive computation with elastic input sequence
Fuzhao Xue, Valerii Likhosherstov, Anurag Arnab, Neil Houlsby, Mostafa Dehghani, and Yang You. 2023 · 2023
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