285 citations · 397 across the 10 of their papers we have counts for
10 papers
Model Equality Testing: Which Model Is This API Serving?
Irena Gao, Percy Liang, Carlos Guestrin
Users often interact with large language models through black-box inference APIs, both for closed- and open-weight models (e.g., Llama models are popularly accessed via Amazon Bedr…
GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling
Hritik Bansal, Po-Nien Kung, P. Jeffrey Brantingham +2
Multimodal event argument role labeling (EARL), a task that assigns a role for each event participant (object) in an image is a complex challenge. It requires reasoning over the en…
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
Anas Awadalla, Irena Gao, Josh Gardner +13
We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replicati…
Are aligned neural networks adversarially aligned?
Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo +8
Large language models are now tuned to align with the goals of their creators, namely to be "helpful and harmless." These models should respond helpfully to user questions, but ref…
Out-of-Domain Robustness via Targeted Augmentations
Irena Gao, Shiori Sagawa, Pang Wei Koh +2
Models trained on one set of domains often suffer performance drops on unseen domains, e.g., when wildlife monitoring models are deployed in new camera locations. In this work, we…
CREPE: Can Vision-Language Foundation Models Reason Compositionally?
Zixian Ma, Jerry Hong, Mustafa Omer Gul +3
A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and lang…
Adaptive Testing of Computer Vision Models
Irena Gao, Gabriel Ilharco, Scott Lundberg +1
Vision models often fail systematically on groups of data that share common semantic characteristics (e.g., rare objects or unusual scenes), but identifying these failure modes is…
Extending the WILDS Benchmark for Unsupervised Adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee +17
Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of…
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund +20
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the…
Fair Matching in Dynamic Kidney Exchange
Irena Gao
Kidney transplants are sharply overdemanded in the United States. A recent innovation to address organ shortages is a kidney exchange, in which willing but medically incompatible p…