Adversarial Nibbler: A DataPerf Challenge for Text-to-Image Models

Hannah Kirk
Jessica Quaye
Charvi Rastogi
Max Bartolo
Oana Inel
Meg Risdal
Will Cukierski
Vijay Reddy
Lora Aroyo


Machine learning progress has been strongly influenced by the data used for model training and evaluation. Only recently however, have development teams shifted their focus more to the data. This shift has been triggered by the numerous reports about biases and errors discovered in AI datasets. Thus, the data-centric AI movement introduced the notion of iterating on the data used in AI systems, as opposed to the traditional model-centric AI approach, which typically treats the data as a given static artifact in model development. With the recent advancement of generative AI, the role of data is even more crucial for successfully developing more factual and safe models. DataPerf challenges follow up on recent successful data- centric challenges drawing attention to the data used for training and evaluation of machine learning model. Specifically, Adversarial Nibbler focuses on data used for safety evaluation of generative text-to-image models. A typical bottleneck in safety evaluation is achieving a representative diversity and coverage of different types of examples in the evaluation set. Our competition aims to gather a wide range of long-tail and unexpected failure modes for text-to-image models in order to identify as many new problems as possible and use various automated approaches to expand the dataset to be useful for training, fine tuning, and evaluation.