54k citations · 58.7k across the 46 of their papers we have counts for
50 papers
Beyond Confidence: Reliable Models Should Also Consider Atypicality
Mert Yuksekgonul, Linjun Zhang, James Zou +1
While most machine learning models can provide confidence in their predictions, confidence is insufficient to understand a prediction's reliability. For instance, the model may hav…
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
Yann Dubois, Xuechen Li, Rohan Taori +6
Large language models (LLMs) such as ChatGPT have seen widespread adoption due to their strong instruction-following abilities. Developing these LLMs involves a complex yet poorly…
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks
Daniel Kang, Xuechen Li, Ion Stoica +3
Recent advances in instruction-following large language models (LLMs) have led to dramatic improvements in a range of NLP tasks. Unfortunately, we find that the same improved capab…
Learning Neural Network Subspaces
Mitchell Wortsman, Maxwell Horton, Carlos Guestrin +2
Recent observations have advanced our understanding of the neural network optimization landscape, revealing the existence of (1) paths of high accuracy containing diverse solutions…
AdaScale SGD: A User-Friendly Algorithm for Distributed Training
Tyler B. Johnson, Pulkit Agrawal, Haijie Gu +1
When using large-batch training to speed up stochastic gradient descent, learning rates must adapt to new batch sizes in order to maximize speed-ups and preserve model quality. Re-…
Set Distribution Networks: a Generative Model for Sets of Images
Shuangfei Zhai, Walter Talbott, Miguel Angel Bautista +2
Images with shared characteristics naturally form sets. For example, in a face verification benchmark, images of the same identity form sets. For generative models, the standard wa…