2018
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Still the right answer for classification, extraction and embedding at small scale. Not every problem needs a generative model.
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.
@inproceedings{brown2020language,
author = {Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and others},
title = {Language Models are Few-Shot Learners},
booktitle = {Advances in Neural Information Processing Systems 33 (NeurIPS 2020)},
year = {2020},
eprint = {2005.14165},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2005.14165}
}Ejentic did not author this paper. Credit belongs to the authors named above; the note is ours.
2018
Still the right answer for classification, extraction and embedding at small scale. Not every problem needs a generative model.
2017
The substrate. Everything on this page assumes it, and the parallelism is the reason the field could scale at all.