2.3k citations · 2.9k across the 20 of their papers we have counts for
4 papers · 1 filter
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference
Eric Mitchell, Joseph J. Noh, Siyan Li +5
While large pre-trained language models are powerful, their predictions often lack logical consistency across test inputs. For example, a state-of-the-art Macaw question-answering…
Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models
Peter Henderson, Eric Mitchell, Christopher D. Manning +2
A growing ecosystem of large, open-source foundation models has reduced the labeled data and technical expertise necessary to apply machine learning to many new problems. Yet found…
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…
Memory-Based Model Editing at Scale
Eric Mitchell, Charles Lin, Antoine Bosselut +2
Even the largest neural networks make errors, and once-correct predictions can become invalid as the world changes. Model editors make local updates to the behavior of base (pre-tr…