Aaron Dant

Large Language Models · 2022

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Large Language Models — 8 citations into it

Whom Aaron Dant trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alec Radford
2. John Zimmerman
3. Mathieu Jacomy

Papers from Aaron Dant's perspective

Ordered by how strongly the researchers Aaron Dant trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
3. Language Models are Unsupervised Multitask Learners
4. Language Models are Few-Shot Learners (Neural Information Processing Systems)
5. Improving Language Understanding by Generative Pre-Training
6. ImageNet classification with deep convolutional neural networks
7. Long Short-Term Memory
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. Gradient-based learning applied to document recognition
10. Language Models as Knowledge Bases?

Their papers

• Ethics, Rules of Engagement, and AI: Neural Narrative Mapping Using Large Transformer Language Models (2022)