Year: 2025
Type: conference-paper
Abstract: Counterfactual data augmentation (CDA) is a method for controlling information or biases in training datasets by generating a complementary dataset with typically opposing biases. Prior work often either relies on hand-crafted rules or algorithmic CDA methods which can leave unwanted information in the augmented dataset. In this work, we show iterative CDA (ICDA) with initial, high-noise interventions can converge to a state with significantly lower noise. Our ICDA procedure produces a dataset where one target signal in the training dataset maintains high mutual information with a corresponding label and the information of spurious signals are reduced. We show training on the augmented datasets produces rationales on documents that better align with human annotation. Our experiments include six human produced datasets and two large-language model generated datasets.
Authors: Mitchell Plyler, Min Chi
Institution: North Carolina State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: National Science Foundation
Awards: Generalizing Data-Driven Technologies to Improve Individualized STEM Instruction by Intelligent Tutors, G6671297155, Integrated Data-driven Technologies for Individualized Instruction in STEM Learning Environments, CAREER: Improving Adaptive Decision Making in Interactive Learning Environments
OpenAlex ID: W4409363552