✨ New Paper ✨ on robust optimization to mitigate unspecified spurious features, accepted to #ICLR2023.
We present AGRO, a novel min-max optimization method that jointly finds coherent error-prone groups in training data and minimizes worst expected loss over them.
🧵🔽 1/6 2/ Human evaluation of ARGO groups in popular benchmark datasets shows that they contain well-defined, yet ✨ previously unstudied ✨ spurious correlations. For e.g., blondes wearing hats or sunglasses in CelebA and MNLI entailment examples with antonyms. More examples in paper.

Excited to share my internship work at Google Research with @iftenney @MatthewRLamm on Retrieval-guided generation of *semantically diverse* counterfactuals for question answering tasks like Natural Questions.
Paper: arxiv.org/abs/2110.07596
[1/n] Automatically generating counterfactuals for QA poses unique challenges: need for world knowledge, semantic diversity, and answerability.
[2/n]