self_supervised_learning_word_embeddings
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self_supervised_learning_word_embeddings [2018/11/28 00:16] burkov |
self_supervised_learning_word_embeddings [2023/12/25 04:15] 135.23.195.80 [Recommended Reading] |
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| * [[https://arxiv.org/pdf/1411.2738v3.pdf|word2vec Parameter Learning Explained]] by Xin Rong (2016) | * [[https://arxiv.org/pdf/1411.2738v3.pdf|word2vec Parameter Learning Explained]] by Xin Rong (2016) | ||
| - | * [[http://rohanvarma.me/Word2Vec/|Language Models, Word2Vec, and Efficient Softmax Approximations]] by | + | * [[http://rohanvarma.me/Word2Vec/|Language Models, Word2Vec, and Efficient Softmax Approximations]] by Rohan Varma (2017) |
| - | Rohan Varma (2017) | + | * [[https://arxiv.org/abs/1706.03762|Attention Is All You Need]] by Vaswani et al. (2017), a state-of-the-art sequence-to-sequence model, plus an [[http://jalammar.github.io/illustrated-transformer/|illustrated guide]] plus an [[http://nlp.seas.harvard.edu/annotated-transformer/|annotated paper with code]]. |
| + | * [[https://arxiv.org/abs/1810.04805|BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]] by Devlin et all (2018) | ||
| + | * [[https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf|Improving Language Understanding by Generative Pre-Training]] by Radford et all (2018) (the GPT paper) | ||
| + | * [[https://arxiv.org/abs/2005.14165|Language Models are Few-Shot Learners]] by Brown et all (2020) (the GPT-3 paper) | ||
| + | * [[https://arxiv.org/abs/2205.11916|Large Language Models are Zero-Shot Reasoners]] by Kojima et al. (2022) (the chain of thought (CoT) prompting paper). | ||
| + | * [[https://arxiv.org/abs/2203.15556|Training Compute-Optimal Large Language Models]] by Hoffmann et al. (2022), (the Chinchilla paper). | ||
| + | * [[https://sebastianraschka.com/blog/2023/llm-reading-list.html|Understanding Large Language Models]] by Sebastian Raschka. | ||
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