Shayne Longpre

Artificial Intelligence · 2016–2024

A researcher in the Science corpus: 16 papers; trusts 325 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 639 citations into it; #23,734 of 35,345 by trust here, #2,038 by citations
• Natural Language Processing — 397 citations into it; #4,935 of 5,688 by trust here, #2,082 by citations
• Large Language Models — 200 citations into it
• Computer Vision and Pattern Recognition — 195 citations into it; #21,534 of 21,575 by trust here, #5,205 by citations
• Evaluation & Benchmarks — 186 citations into it
• Information Retrieval & Search — 34 citations into it
• Model Architectures — 34 citations into it
• Representation & Self-Supervised Learning — 33 citations into it

Whom Shayne Longpre trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Kaiming He
6. Alex Krizhevsky
7. Richard Socher
8. Yann LeCun
9. Jacob Devlin
10. Alex Graves

Who trusts Shayne Longpre overall

1. Akari Asai
2. Steven Y. Feng
3. John Miller
4. James Bradbury
5. Gautier Izacard
6. Weijia Shi
7. Tim Dettmers
8. Jungo Kasai
9. Adam Fisch
10. Jason Wei

Papers from Shayne Longpre's perspective

Ordered by how strongly the researchers Shayne Longpre 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. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
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. 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

• Scaling Instruction-Finetuned Language Models (2022) — 4,301 citations
• The Flan Collection: Designing Data and Methods for Effective Instruction Tuning (2023) — 953 citations
• Open-Domain Question Answering Goes Conversational via Question Rewriting (2020) — 230 citations
• Entity-Based Knowledge Conflicts in Question Answering (2021) — 396 citations
• How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers? (2020) — 94 citations
• An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering (2019) — 74 citations
• MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2020) — 226 citations
• The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset (2023) — 217 citations
• A Way out of the Odyssey: Analyzing and Combining Recent Insights for LSTMs (2016) — 10 citations
• Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model (2024) — 385 citations
• Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation (2024) — 178 citations
• Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models (2024) — 471 citations
• Prometheus: Inducing Fine-grained Evaluation Capability in Language Models (2023) — 564 citations
• You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings (2022)
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
• A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering (2020) — 14 citations