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Papers that changed AI, with a plain-language explainer for each.

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2020
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
arXiv
A 2020 paper by Clark, Luong, Le and Manning that pre-trains text encoders by detecting replaced tokens instead of filling in masked ones, matching much larger baselines with far less compute.
Large language models Research history Deep learning
2020
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
arXiv
A 2020 paper by Mildenhall, Srinivasan, Tancik, Barron, Ramamoorthi and Ng that represents a scene as a neural radiance field: a network mapping 5D coordinates to density and colour, optimized from a sparse set of images with known camera poses.
Research history Generative models Computer vision
2020
Scaling Laws for Neural Language Models
arXiv
A 2020 paper finding that language model loss falls predictably, as a power law, with model size, data and compute. It gave labs a quantitative reason to build much larger models and shaped the scale-up that followed.
Large language models Foundation models Hardware & compute
2019
C4: Colossal Clean Crawled Corpus (T5 paper)
JMLR
A 2019 paper that casts every language task as text-to-text, systematically compares transfer-learning choices, releases the C4 corpus and models, and reports state-of-the-art results on summarization, question answering, and classification.
Large language models Research history Data & datasets
2019
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (T5)
JMLR
A 2019 Google paper that frames every language task as text in, text out, using one encoder-decoder Transformer and one training setup. It also introduced the C4 dataset, and the released T5 models became common research starting points.
Large language models Open source Data & datasets
2019
RoBERTa: A Robustly Optimized BERT Pretraining Approach
arXiv
A replication study of BERT pretraining that measures how much key hyperparameters and training data size matter, finds BERT was significantly undertrained, and shows a properly trained BERT matches or beats later models, reaching state of the art on GLUE, RACE and SQuAD.
Large language models Research history Deep learning
2019
XLNet: Generalized Autoregressive Pretraining for Language Understanding
arXiv
XLNet is a generalized autoregressive pretraining method from a 2019 paper. It learns bidirectional contexts by maximizing expected likelihood over all factorization-order permutations and outperforms BERT on 20 tasks under comparable settings.
Large language models Research history Deep learning
2019
HellaSwag: Can a Machine Really Finish Your Sentence?
arXiv
HellaSwag is a 2019 benchmark in which a machine must pick the most plausible continuation of a short event description. Its wrong answers are generated and filtered to fool models rather than people.
Large language models Research history Reasoning
2019
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
arXiv
SuperGLUE is a benchmark for general-purpose language understanding, released in 2019 as a harder successor to GLUE. It bundles eight tasks, a single-number metric, an analysis toolkit and a public leaderboard.
Large language models Research history Data & datasets
2019
Language Models are Unsupervised Multitask Learners (GPT-2)
OpenAI
OpenAI's 2019 paper on GPT-2, a 1.5-billion-parameter Transformer trained on web text. It showed that a model trained only to predict the next word could perform several tasks when prompted, and its staged release sparked public debate.
Large language models Foundation models Safety & governance
2018
A Style-Based Generator Architecture for Generative Adversarial Networks
arXiv
This 2018 paper introduces a style-based GAN generator that separates high-level attributes from stochastic variation and allows scale-specific control, improving distribution quality, interpolation, and disentanglement.
Generative models Computer vision Image generation
2018
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
NAACL
Google's 2018 BERT paper introduced an encoder-only Transformer that reads text in both directions and is pre-trained by hiding words. It set new results on eleven language tasks and made download-and-fine-tune the default workflow.
Large language models Research history Foundation models
2018
Large Scale GAN Training for High Fidelity Natural Image Synthesis
arXiv
A 2018 paper by Andrew Brock, Jeff Donahue and Karen Simonyan that trained generative adversarial networks at the largest scale attempted at the time, introduced BigGANs and set a new state of the art in class-conditional image synthesis.
Generative models Computer vision Image generation
2018
Glow: Generative Flow with Invertible 1x1 Convolutions
arXiv
A 2018 paper by Diederik P. Kingma and Prafulla Dhariwal presenting Glow, a flow-based generative model built on invertible 1x1 convolutions, which improved log-likelihood on standard benchmarks and could synthesise and manipulate large images.
Research history Generative models Image generation
2018
Improving Language Understanding by Generative Pre-Training (GPT)
OpenAI
OpenAI's 2018 paper introducing GPT, a Transformer decoder pre-trained to predict the next token and then fine-tuned for specific tasks. It improved the state of the art on 9 of 12 tasks studied and set the recipe the GPT series followed.
Large language models Research history Foundation models
2018
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
arXiv
GLUE is a benchmark and analysis platform that evaluates natural language understanding models across a diverse set of existing tasks, with an online comparison platform and a hand-crafted diagnostic suite for linguistic analysis.
Large language models Research history Data & datasets
2017
Neural Discrete Representation Learning
arXiv
A 2017 paper by van den Oord, Vinyals and Kavukcuoglu proposing VQ-VAE, a generative model whose latent code is discrete rather than continuous, and whose prior is learned rather than fixed, so that useful representations can be learned without supervision.
Research history Generative models Image generation
2017
Progressive Growing of GANs for Improved Quality, Stability, and Variation
arXiv
A 2017 paper by Karras, Aila, Laine and Lehtinen that trains generative adversarial networks by growing the generator and discriminator from low to high resolution, improving stability, quality and variation.
Generative models Computer vision Image generation
2017
Deep reinforcement learning from human preferences
NeurIPS
A 2017 paper showing that an AI agent can learn a task from short human comparisons instead of a hand-written reward. The method, reinforcement learning from human feedback, later became central to training chat assistants.
Large language models Research history Safety & governance
2017
Attention Is All You Need
NeurIPS
A 2017 paper that introduced the Transformer, a neural network built entirely on attention with no recurrence. It set new machine translation results and became the architecture behind almost every large language model since.
Large language models Research history Deep learning