Mirac Suzgun

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 12 papers; trusts 84 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 313 citations into it; #33,618 of 35,345 by trust here, #1,596 by citations
• Natural Language Processing — 155 citations into it
• Large Language Models — 109 citations into it
• Computer Vision and Pattern Recognition — 84 citations into it
• Evaluation & Benchmarks — 70 citations into it
• Information Systems — 34 citations into it
• Model Architectures — 23 citations into it
• Electrical and Electronic Engineering — 5 citations into it

Whom Mirac Suzgun trusts overall

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

1. P. Rodríguez
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Sepp Hochreiter
6. Yann LeCun
7. Diederik P. Kingma
8. J. Urgen Schmidhuber
9. Alex Krizhevsky
10. Alex Graves

Who trusts Mirac Suzgun overall

1. Michael Hahn
2. Xinyun Chen
3. Jie Huang
4. Benjamin Newman
5. Zhihong Shao
6. Freda Shi
7. Jason Wei
8. Yi Tay
9. Or Honovich
10. Denny Zhou

Papers from Mirac Suzgun's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
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

• Scaling Instruction-Finetuned Language Models (2022) — 4,301 citations
• Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them (2022) — 2,044 citations
• Language Models are Multilingual Chain-of-Thought Reasoners (2022) — 634 citations
• Holistic Evaluation of Language Models (2023) — 1,863 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations
• Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study. (2024) — 430 citations
• Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools (2024) — 341 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2023) — 266 citations
• Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions (2023) — 413 citations
• Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models (2022) — 74 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations
• On Evaluating the Generalization of LSTM Models in Formal Languages (2018) — 48 citations