Sam Bowman
Artificial Intelligence · 2017–2023
A researcher in the Science corpus: 19 papers; trusts 79 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 532 citations into it; #26,368 of 35,345 by trust here, #1,827 by citations
- Natural Language Processing — 281 citations into it; #4,925 of 5,688 by trust here, #1,466 by citations
- Evaluation & Benchmarks — 151 citations into it; #3,087 of 3,147 by trust here, #993 by citations
- Computer Vision and Pattern Recognition — 142 citations into it
Whom Sam Bowman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Alicia Parrish
- Paul Christiano
- Yann LeCun
- Kaiming He
- Alex Graves
Who trusts Sam Bowman overall
Papers from Sam Bowman's perspective
Ordered by how strongly the researchers Sam Bowman trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Language Models are Unsupervised Multitask Learners
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Constitutional AI: Harmlessness from AI Feedback (2022) — 3,377 citations
- Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned (2022) — 837 citations
- BBQ: A hand-built bias benchmark for question answering (2021) — 808 citations
- Language Models (Mostly) Know What They Know (2022) — 1,782 citations
- Discovering Language Model Behaviors with Model-Written Evaluations (2022) — 917 citations
- Pretraining Language Models with Human Preferences (2023) — 302 citations
- The Capacity for Moral Self-Correction in Large Language Models (2023) — 214 citations
- Inverse Scaling: When Bigger Isn't Better (2023) — 235 citations
- QuALITY: Question Answering with Long Input Texts, Yes! (2021) — 240 citations
- What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? (2021) — 38 citations
- Measuring Progress on Scalable Oversight for Large Language Models (2022) — 226 citations
- What Do NLP Researchers Believe? Results of the NLP Community Metasurvey (2022) — 48 citations
- Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions (2022) — 17 citations
- Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting (2023) — 1,359 citations
- Studying Large Language Model Generalization with Influence Functions (2023) — 343 citations
- Measuring Faithfulness in Chain-of-Thought Reasoning (2023) — 499 citations
- Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions (2022) — 21 citations
- Comparing Test Sets with Item Response Theory (2021) — 64 citations
- Universal Dependencies 2.1 (2017) — 425 citations