Angelica Chen

Artificial Intelligence · 2021–2024

A researcher in the Science corpus: 13 papers; trusts 317 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 355 citations into it; #8,313 of 35,345 by trust here, #7,471 by citations
• Natural Language Processing — 230 citations into it; #5,480 of 5,688 by trust here, #2,732 by citations
• Large Language Models — 111 citations into it
• Evaluation & Benchmarks — 95 citations into it; #3,090 of 3,147 by trust here, #527 by citations
• Computer Vision and Pattern Recognition — 87 citations into it
• Safety, Robustness & Interpretability — 63 citations into it; #2,210 of 2,251 by trust here, #1,454 by citations
• Reinforcement Learning — 52 citations into it
• Information Systems — 41 citations into it; #1,502 of 8,771 by trust here, #6,770 by citations

Whom Angelica Chen trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alec Radford
4. Diederik P. Kingma
5. Ilya Sutskever
6. Alex Krizhevsky
7. Kaiming He
8. Yann LeCun
9. J'er'emy Scheurer
10. Alex Graves

Who trusts Angelica Chen overall

1. Xinyun Chen
2. Zeqiu Wu
3. Yann Dubois
4. Stephen Casper
5. Maxwell Lin
6. Xuechen Li
7. J'er'emy Scheurer
8. Yushi Hu
9. Tomasz Korbak
10. Ahmed El-Kishky

Papers from Angelica Chen's perspective

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

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

Their papers

• BBQ: A hand-built bias benchmark for question answering (2021) — 808 citations
• Improving Code Generation by Training with Natural Language Feedback (2023) — 97 citations
• Pretraining Language Models with Human Preferences (2023) — 302 citations
• QuALITY: Question Answering with Long Input Texts, Yes! (2021) — 240 citations
• Training Language Models with Language Feedback at Scale (2023) — 138 citations
• What Do NLP Researchers Believe? Results of the NLP Community Metasurvey (2022) — 48 citations
• SQuALITY: Building a Long-Document Summarization Dataset the Hard Way (2022) — 59 citations
• Learning from Natural Language Feedback (2024) — 29 citations
• Training Language Models with Natural Language Feedback (2022)
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations
• Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions (2022) — 21 citations
• Training Language Models with Language Feedback (2022) — 58 citations
• Adversarially Constructed Evaluation Sets Are More Challenging, but May Not Be Fair (2021) — 15 citations