Cunxiang Wang
Artificial Intelligence · 2019–2025
A researcher in the Science corpus: 8 papers; trusts 180 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 344 citations into it; #12,550 of 35,345 by trust here, #6,796 by citations
- Natural Language Processing — 138 citations into it; #2,652 of 5,688 by trust here, #3,053 by citations
- Large Language Models — 126 citations into it
- Evaluation & Benchmarks — 107 citations into it; #2,631 of 3,147 by trust here, #1,355 by citations
- Computer Vision and Pattern Recognition — 101 citations into it
- Information Retrieval & Search — 18 citations into it
- General Social Sciences — 14 citations into it
Whom Cunxiang Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Jacob Devlin
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alec Radford
- Alex Krizhevsky
- Richard Socher
- Ernest Davis
- Kaiming He
Who trusts Cunxiang Wang overall
- Timo Schick
- Bodhisattwa Prasad Majumder
- Bill Yuchen Lin
- Tara Safavi
- Peter Hase
- Hinrich Schütze
- Oana-Maria Camburu
- Veronica Latcinnik
- W. Yu
- Yue Zhang
Papers from Cunxiang Wang's perspective
Ordered by how strongly the researchers Cunxiang Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- A Survey on Evaluation of Large Language Models (2023) — 3,582 citations
- Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation (2019) — 113 citations
- PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization (2023) — 400 citations
- SemEval-2020 Task 4: Commonsense Validation and Explanation (2020) — 105 citations
- Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA? (2021) — 95 citations
- Deep Literature Survey Automation with an Iterative Workflow (2025)
- Knowledge Conflicts for LLMs: A Survey (2024) — 301 citations
- Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoning (2021) — 24 citations