David Rosenbluth

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 David Rosenbluth trusts overall

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

1. Alec Radford

Papers from David Rosenbluth's perspective

Ordered by how strongly the researchers David Rosenbluth 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. Language Models as Knowledge Bases?
7. How Can We Know What Language Models Know?
8. T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples
9. Research through design as a method for interaction design research in HCI (International Conference on Human Factors in Computing Systems)
10. A Survey on Bias and Fairness in Machine Learning (ACM Computing Surveys)

Their papers

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