Probabilistic Robustness for Data Filtering

Yu Yu, Abdul Rafae Khan, Shahram Khadivi, Jia Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We introduce our probabilistic robustness rewarded data optimization (PRoDO) approach as a framework to enhance the model's generalization power by selecting training data that optimizes our probabilistic robustness metrics. We use proximal policy optimization (PPO) reinforcement learning to approximately solve the computationally intractable training subset selection problem. The PPO's reward is defined as our (α, ϵ, γ)-Robustness that measures performance consistency over multiple domains by simulating unknown test sets in real-world scenarios using a leaving-one-out strategy. We demonstrate that our PRoDO effectively filters data that lead to significantly higher prediction accuracy and robustness on unknown-domain test sets. Our experiments achieve up to +17.2% increase of accuracy (+25.5% relatively) in sentiment analysis, and - 28.05 decrease of perplexity (-32.1% relatively) in language modeling. In addition, our probabilistic (α, ϵ, γ)-Robustness definition serves as an evaluation metric with higher levels of agreement with human annotations than typical performance-based metrics.

Original languageEnglish
Title of host publicationEACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
Pages2942-2951
Number of pages10
ISBN (Electronic)9781959429449
StatePublished - 2023
Event17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 - Dubrovnik, Croatia
Duration: 2 May 20236 May 2023

Publication series

NameEACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023
Country/TerritoryCroatia
CityDubrovnik
Period2/05/236/05/23

Fingerprint

Dive into the research topics of 'Probabilistic Robustness for Data Filtering'. Together they form a unique fingerprint.

Cite this