Dually Self-Improved Counterfactual Data Augmentation Using Large Language Model

ACL ARR 2025 February Submission384 Authors

Abstract: Counterfactual data augmentation, which generates minimally edited tokens to alter labels, has become a key approach to improving model robustness in natural language processing (NLP). It is usually implemented by first identifying the causal terms and then modifying these terms to create counterfactual candidates. The emergence of large language models (LLMs) has effectively facilitated the task of counterfactual data augmentation. However, existing LLM-based approaches still face some challenges in 1) accurately extracting the task-specific causal terms, and 2) the quality of LLM-generated counterfacts. To address the issues, we propose a dually self-improved counterfactual data augmentation method using LLM for Natural Language Inference (NLI) task. On the one hand, we design a self-improved strategy employing the attention distribution of the task model to identify the task-specific causal terms, which is lightweight and task-specific. On the other hand, a second self-improved strategy based on direct preference optimization is utilized to refine LLM-generated counterfacts, achieving high-quality counterfacts. Finally, a balanced loss preventing over-emphasis on augmentated data is proposed to retrain the task model on the fusion of existing data and generated counterfacts. Extensive experiments on NLI benchmarks demonstrate the effectiveness of our proposed method in generating high-quality counterfacts for improving task performance.

Paper Type: Long

Research Area: Efficient/Low-Resource Methods for NLP

Research Area Keywords: counterfactual data augmentation, attention, large language model, self-improvement

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 384