Abhilasha Ravichander
Artificial Intelligence · 2018–2024
A researcher in the Science corpus: 13 papers; trusts 376 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 398 citations into it; #7,728 of 35,345 by trust here, #4,772 by citations
- Natural Language Processing — 305 citations into it; #1,578 of 5,688 by trust here, #1,305 by citations
- Evaluation & Benchmarks — 122 citations into it; #901 of 3,147 by trust here, #987 by citations
- Large Language Models — 107 citations into it
- Computer Vision and Pattern Recognition — 100 citations into it
- Representation & Self-Supervised Learning — 45 citations into it; #2,816 of 3,145 by trust here, #2,757 by citations
- Prompting & Reasoning — 36 citations into it
- Safety, Robustness & Interpretability — 30 citations into it
Whom Abhilasha Ravichander trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Tomas Mikolov
- Ilya Sutskever
- Christopher D. Manning
- Alex Krizhevsky
- Sepp Hochreiter
- Yann LeCun
- Kaiming He
Who trusts Abhilasha Ravichander overall
- Yonatan Belinkov
- Xiang Zhou
- Rodrigo Nogueira
- James Thorne
- Yanai Elazar
- Eric Wallace
- Wes Gurnee
- Adina Williams
- Yixin Nie
- Nora Kassner
Papers from Abhilasha Ravichander's perspective
Ordered by how strongly the researchers Abhilasha Ravichander 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- 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
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Stress Test Evaluation for Natural Language Inference (2018) — 420 citations
- Measuring and Improving Consistency in Pretrained Language Models (2021) — 487 citations
- OLMo: Accelerating the Science of Language Models (2024) — 700 citations
- Exploring Numeracy in Word Embeddings (2019) — 65 citations
- Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research (2024) — 519 citations
- Measuring Causal Effects of Data Statistics on Language Model's 'Factual' Predictions (2022) — 67 citations
- Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? (2020) — 169 citations
- Overview and Insights from the Shared Tasks at Scholarly Document Processing 2020: CL-SciSumm, LaySumm and LongSumm (2020) — 46 citations
- The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning (2023) — 325 citations
- WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild (2024) — 229 citations
- CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation (2022) — 49 citations
- On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT (2020) — 92 citations
- EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference (2019) — 87 citations