2.3k citations · 3.6k across the 27 of their papers we have counts for
29 papers
Ask Me Anything: A simple strategy for prompting language models
Simran Arora, Avanika Narayan, Mayee F. Chen +6
Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training.…
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions
Lingjiao Chen, Zhihua Jin, Sabri Eyuboglu +3
Commercial ML APIs offered by providers such as Google, Amazon and Microsoft have dramatically simplified ML adoption in many applications. Numerous companies and academics pay to…
LegalBench: Prototyping a Collaborative Benchmark for Legal Reasoning
Neel Guha, Daniel E. Ho, Julian Nyarko +1
Can foundation models be guided to execute tasks involving legal reasoning? We believe that building a benchmark to answer this question will require sustained collaborative effort…
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
William Chen, Siyi Hu, Rajat Talak +1
Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of a…
Contrastive Adapters for Foundation Model Group Robustness
Michael Zhang, Christopher Ré
While large pretrained foundation models (FMs) have shown remarkable zero-shot classification robustness to dataset-level distribution shifts, their robustness to subpopulation or…
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Tri Dao, Daniel Y. Fu, Stefano Ermon +2
Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods ha…