# Model Collection

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  This section is under heavy development.
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This section consists of a collection and summary of notable and foundational LLMs.



## Models

| Model | Description | 
| --- | --- | 
| [BERT](https://arxiv.org/abs/1810.04805) | Bidirectional Encoder Representations from Transformers | 
| [RoBERTa](https://arxiv.org/abs/1907.11692) | A Robustly Optimized BERT Pretraining Approach | 
| [ALBERT](https://arxiv.org/abs/1909.11942) | A Lite BERT for Self-supervised Learning of Language Representations | 
| [XLNet](https://arxiv.org/abs/1906.08237) | Generalized Autoregressive Pretraining for Language Understanding and Generation |
| [GPT](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) | Improving Language Understanding by Generative Pre-Training | 
| [GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) | Language Models are Unsupervised Multitask Learners | 
| [GPT-3](https://arxiv.org/abs/2005.14165) | Language Models are Few-Shot Learners |
| [T5](https://arxiv.org/abs/1910.10683) | Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer | 
| [CTRL](https://arxiv.org/abs/1909.05858) | CTRL: A Conditional Transformer Language Model for Controllable Generation | 
| [BART](https://arxiv.org/abs/1910.13461) | Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension |
| [Chinchilla](https://arxiv.org/abs/2203.15556)(Hoffman et al. 2022) | Shows that for a compute budget, the best performances are not achieved by the largest models but by smaller models trained on more data. |
