The papers behind how we build

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3 entries

1. 2020NeurIPS 2020PublishedCurated

   Language Models are Few-Shot Learners

   Brown et al.

   Demonstrates that a sufficiently large language model can perform a new task from a handful of examples in the prompt, with no gradient updates. The paper established in-context learning as the default interface to a language model.

   Foundations

   arXiv
2. 2018NAACL-HLT 2019PublishedCurated

   BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

   Devlin et al.

   Pre-trains a bidirectional transformer by masking tokens and predicting them from both directions, then fine-tunes the same weights on downstream tasks. The masked objective, and the idea of pre-train then fine-tune, set the template for the following years of NLP.

   Foundations

   arXiv
3. 2017NeurIPS 2017PublishedCurated

   Attention Is All You Need

   Vaswani et al.

   Introduces the transformer: a sequence model built entirely from attention, with no recurrence and no convolution. Because every position is computed in parallel, the architecture scales to far larger training runs than the recurrent models it replaced.

   Foundations

   arXiv

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