266 citations · 614 across the 24 of their papers we have counts for
10 papers · 1 filter
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
Zichang Liu, Jue Wang, Tri Dao +8
Large language models (LLMs) with hundreds of billions of parameters have sparked a new wave of exciting AI applications. However, they are computationally expensive at inference t…
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
Neel Guha, Julian Nyarko, Daniel E. Ho +37
The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform? To enable great…
Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models
Mayee F. Chen, Nicholas Roberts, Kush Bhatia +4
The quality of training data impacts the performance of pre-trained large language models (LMs). Given a fixed budget of tokens, we study how to best select data that leads to good…
Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification
Neel Guha, Mayee F. Chen, Kush Bhatia +3
Recent work has shown that language models' (LMs) prompt-based learning capabilities make them well suited for automating data labeling in domains where manual annotation is expens…
Fast Algorithms for a New Relaxation of Optimal Transport
Moses Charikar, Beidi Chen, Christopher Re +1
We introduce a new class of objectives for optimal transport computations of datasets in high-dimensional Euclidean spaces. The new objectives are parametrized by , and pr…
Towards trustworthy seizure onset detection using workflow notes
Khaled Saab, Siyi Tang, Mohamed Taha +3
A major barrier to deploying healthcare AI models is their trustworthiness. One form of trustworthiness is a model's robustness across different subgroups: while existing models ma…