Benjamin Mann

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 14 papers; trusts 8 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 497 citations into it; #10,185 of 35,345 by trust here, #125 by citations

Whom Benjamin Mann trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Sepp Hochreiter
5. Oriol Vinyals
6. Ilya Sutskever
7. Alex Krizhevsky
8. Alex Graves
9. Yann LeCun
10. Kaiming He

Who trusts Benjamin Mann overall

1. Yi Tay
2. Sewon Min
3. Renrui Zhang
4. Michael Lewis
5. Luke S. Zettlemoyer
6. Mohit Bansal
7. Zhe Gan
8. Swaroop Mishra
9. Percy Liang
10. Yoshua Bengio

Papers from Benjamin Mann's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Language Models are Few-Shot Learners (2020) — 61,005 citations
• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback (2022) — 4,254 citations
• Constitutional AI: Harmlessness from AI Feedback (2022) — 3,377 citations
• Scaling Laws for Autoregressive Generative Modeling (2020) — 639 citations
• Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned (2022) — 837 citations
• Predictability and Surprise in Large Generative Models (2022) — 365 citations
• In-context Learning and Induction Heads (2022) — 942 citations
• Language Models (Mostly) Know What They Know (2022) — 1,782 citations
• Discovering Language Model Behaviors with Model-Written Evaluations (2022) — 917 citations
• The Capacity for Moral Self-Correction in Large Language Models (2023) — 214 citations
• Measuring Progress on Scalable Oversight for Large Language Models (2022) — 226 citations
• Scaling Laws and Interpretability of Learning from Repeated Data (2022) — 166 citations
• Moving the Eiffel Tower to ROME: Tracing and Editing Facts in GPT (2021)
• A General Language Assistant as a Laboratory for Alignment (2021) — 1,175 citations