Fetching the paper…
Reading the bibliography…
We investigate the role of uncertainty in decision-making problems with natural language as input.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
Earlier work this paper cites.
Bayesian interpolation
David JC MacKay · 1992
Earlier work this paper cites.
Bandit problems with side observations
Chih-Chun Wang, Sanjeev R Kulkarni, and H Vincent Poor · 2005
Earlier work this paper cites.
SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter
Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, and Manuela Sanguinetti · 2007
Earlier work this paper cites.
Bayesian inference in statistical analysis
George EP Box and George C Tiao · 2011
Earlier work this paper cites.
An empirical evaluation of thompson sampling
Olivier Chapelle and Lihong Li · 2011
Earlier work this paper cites.
Practical variational inference for neural networks
Alex Graves · 2011
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts · 2011
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Bayesian convolutional neural networks with bernoulli approximate variational inference
Yarin Gal and Zoubin Ghahramani · 2015
Earlier work this paper cites.
Uncertainty in deep learning
Yarin Gal · 2016
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Earlier work this paper cites.
Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
An information-theoretic analysis of thompson sampling
Daniel Russo and Benjamin Van Roy · 2016
Earlier work this paper cites.
Near-optimal regret bounds for thompson sampling
Shipra Agrawal and Navin Goyal · 2017
Earlier work this paper cites.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Parallelised bayesian optimisation via thompson sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, and Barnabás Póczos · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Cited alongside, same era.
Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling
Carlos Riquelme, George Tucker, and Jasper Snoek · 2018
Cited alongside, same era.
Bandits for online calibration: An application to content moderation on social media platforms
Vashist Avadhanula, Omar Abdul Baki, Hamsa Bastani, Osbert Bastani, Caner Gocmen, Daniel Haimovich, Darren Hwang, Dima Karamshuk, Thomas Leeper, Jiayuan Ma, et al · 2022
Later among the works it cites.
Pre-trained language models for interactive decision-making
Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, et al · 2022
Later among the works it cites.
Fine-tuning language models via epistemic neural networks
Ian Osband, Seyed Mohammad Asghari, Benjamin Van Roy, Nat McAleese, John Aslanides, and Geoffrey Irving · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback, 2022
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Daniel J Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen, et al · 2018
Cited alongside, same era.
Explicit inductive bias for transfer learning with convolutional networks
LI Xuhong, Yves Grandvalet, and Franck Davoine · 2018
Cited alongside, same era.
Limitations of the empirical fisher approximation for natural gradient descent
Frederik Kunstner, Philipp Hennig, and Lukas Balles · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval)
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Algorithmic content moderation: Technical and political challenges in the automation of platform governance
Robert Gorwa, Reuben Binns, and Christian Katzenbach · 2020
Cited alongside, same era.
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al · 2022
Later among the works it cites.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
Later among the works it cites.
Grounding large language models in interactive environments with online reinforcement learning
Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer · 2023
Later among the works it cites.
Introspective tips: Large language model for in-context decision making
Liting Chen, Lu Wang, Hang Dong, Yali Du, Jie Yan, Fangkai Yang, Shuang Li, Pu Zhao, Si Qin, Saravan Rajmohan, et al · 2023
Later among the works it cites.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2023
Later among the works it cites.
Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
Later among the works it cites.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Later among the works it cites.
Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings
Shibo Hao, Tianyang Liu, Zhen Wang, and Zhiting Hu · 2023
Later among the works it cites.
Motif: Intrinsic motivation from artificial intelligence feedback
Martin Klissarov, Pierluca D’Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, and Mikael Henaff · 2023
Later among the works it cites.
Augmented language models: a survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al · 2023
Later among the works it cites.
Gpt-4 technical report, 2023
OpenAI · 2023
Later among the works it cites.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
Later among the works it cites.
Efficient exploration for llms
Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao, and Benjamin Van Roy · 2024
Closest in time.