DeepAI AI Chat
Log In Sign Up

Retrieval-guided Counterfactual Generation for QA

by   Bhargavi Paranjape, et al.

Deep NLP models have been shown to learn spurious correlations, leaving them brittle to input perturbations. Recent work has shown that counterfactual or contrastive data – i.e. minimally perturbed inputs – can reveal these weaknesses, and that data augmentation using counterfactuals can help ameliorate them. Proposed techniques for generating counterfactuals rely on human annotations, perturbations based on simple heuristics, and meaning representation frameworks. We focus on the task of creating counterfactuals for question answering, which presents unique challenges related to world knowledge, semantic diversity, and answerability. To address these challenges, we develop a Retrieve-Generate-Filter(RGF) technique to create counterfactual evaluation and training data with minimal human supervision. Using an open-domain QA framework and question generation model trained on original task data, we create counterfactuals that are fluent, semantically diverse, and automatically labeled. Data augmentation with RGF counterfactuals improves performance on out-of-domain and challenging evaluation sets over and above existing methods, in both the reading comprehension and open-domain QA settings. Moreover, we find that RGF data leads to significant improvements in a model's robustness to local perturbations.


page 1

page 2

page 3

page 4


CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation

Counterfactual data augmentation (CDA) – i.e., adding minimally perturbe...

DISCO: Distilling Phrasal Counterfactuals with Large Language Models

Recent methods demonstrate that data augmentation using counterfactual k...

CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration

In recent years, large language models (LLMs) have shown remarkable capa...

IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions

Although counterfactual reasoning is a fundamental aspect of intelligenc...

Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions

Contrast consistency, the ability of a model to make consistently correc...

CREST: A Joint Framework for Rationalization and Counterfactual Text Generation

Selective rationales and counterfactual examples have emerged as two eff...