C
Christopher R'e
Publications - 16
Citations - 609
Christopher R'e is an academic researcher. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 10, co-authored 16 publications receiving 609 citations.
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Journal ArticleDOI
Holistic Evaluation of Language Models
Percy Liang,Rishi Bommasani,Tony Lee,Dimitris Tsipras,Dilara Soylu,Michihiro Yasunaga,Yian Zhang,Deepak Narayanan,Yuhuai Wu,Ananya Kumar,Benjamin Newman,Binhang Yuan,Bobby Yan,Ce Zhang,Christian Cosgrove,Christopher D. Manning,Christopher R'e,Diana Acosta-Navas,Drew A. Hudson,Eric Zelikman,Esin Durmus,Faisal Ladhak,Frieda Rong,Hongyu Ren,Huaxiu Yao,Jue Wang,Keshav Santhanam,Laurel Orr,Lucia Zheng,Byron Rogers,Mirac M. Suzgun,Nathan S. Kim,Neel Guha,Niladri S. Chatterji,Peter Henderson,Qian Huang,Ryan Chi,Michael Xie,Shibani Santurkar,Surya Ganguli,Tatsunori Hashimoto,Thomas Icard,Tianyi Zhang,Vishrav Chaudhary,William Wang,Xuechen Li,Yifan Mai,Yuhui Zhang,Yuta Koreeda +48 more
TL;DR: The Holistic Evaluation of Language Models (HELM) as mentioned in this paper ) is a popular benchmark for language models, with 30 models evaluated on 16 core scenarios and 7 metrics, exposing important trade-offs.
Proceedings ArticleDOI
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
TL;DR: This work proposes FlashAttention, an IO-aware exact attention algorithm that uses tiling to reduce the number of memory reads/writes between GPU high bandwidth memory (HBM) and GPU on-chip SRAM, and is optimal for a range of SRAM sizes.
Proceedings Article
It's Raw! Audio Generation with State-Space Models
TL;DR: SaShiMi, a new multi-scale architecture for waveform modeling built around the recently introduced S4 model for long sequence modeling, is proposed, identifying that S4 can be unstable during autoregressive generation, and providing a simple improvement to its parameterization by drawing connections to Hurwitz matrices.
Proceedings ArticleDOI
Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations
TL;DR: Correct- N -Contrast (C N C), a contrastive learning method to improve robustness to spurious correlations when training group labels are unknown, improves worst-group accuracy over existing state-of-the-art methods on popular benchmarks, and performs almost as well as methods trained with group labels.
Proceedings ArticleDOI
Ask Me Anything: A simple strategy for prompting language models
Simran Arora,Avanika Narayan,Mayee F. Chen,Laurel Orr,Neel Guha,Kush S. Bhatia,Ines Chami,Frederic Sala,Christopher R'e +8 more
TL;DR: This paper develops an understanding of the effective prompt formats and proposes to use weak supervision, a procedure for combining the noisy predictions, to produce the final predictions of the GPT-Neo-6B model.