LM with RNNs
Different models have been studied, starting from the initial recurrent neural network based language model (Mikolov et al. 2011). Recurrent neural networks
LSTM were then used with more success than previous models (Zaremba et al. 2015).
Recently, transformers seem to have dominated language modeling. However it is not clear if this is due to their real superiority over RNNs or their practical scalability (Merity 2019).
LM with Transformers
- GPT-2, introduced in (Radford et al. 2019) and on this page.
- GPT-3, introduced in (Brown et al. 2020).
Language modeling and Compression
Language models can be used to generate text from a prompt or starting sentence. This is the kind of examples that made models like GPT-2 and GPT-3 famous, because of their ability to generate long sequences of apparently coherent text (Radford et al. 2019; Brown et al. 2020).
Language modeling for Automated theorem proving
Language modeling for Reinforcement Learning
- Tomas Mikolov, Martin Karafiat, Lukas Burget, Jan Cernocky, Sanjeev Khudanpur. . "Recurrent Neural Network Based Language Model". In , 4.
- Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals. . "Recurrent Neural Network Regularization". Arxiv:1409.2329 [cs]. http://arxiv.org/abs/1409.2329.
- Stephen Merity. . "Single Headed Attention RNN: Stop Thinking with Your Head". Arxiv:1911.11423 [cs]. http://arxiv.org/abs/1911.11423.
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever. . "Language Models Are Unsupervised Multitask Learners". Openai Blog 1 (8):9.
- Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, et al.. . "Language Models Are Few-shot Learners". Arxiv:2005.14165 [cs]. http://arxiv.org/abs/2005.14165.
- Stanislas Polu, Ilya Sutskever. . "Generative Language Modeling for Automated Theorem Proving". Arxiv:2009.03393 [cs, Stat]. http://arxiv.org/abs/2009.03393.
- Michael Janner, Qiyang Li, Sergey Levine. . "Reinforcement Learning as One Big Sequence Modeling Problem". Arxiv:2106.02039 [cs]. http://arxiv.org/abs/2106.02039.