Guiding LLMs to Generate High-Fidelity and High-Quality Counterfactual Explanations for Text Classification

2025-03-06

AI Summary

Generating high-fidelity counterfactual explanations for text classification without fine-tuning the classifier remains challenging. Method: We propose an LLM-guided lightweight counterfactual generation framework that integrates frozen classifier decision signals into LLaMA/GPT inference via gradient-aware prompting and output-space constraints, enabling faithful label-flipping. Contribution/Results: We introduce two novel fine-tuning-free classifier-guidance mechanisms; empirically reveal that LLMs inherently rely more on parametric knowledge than on classifier logic during counterfactual generation; and demonstrate that counterfactual data augmentation improves classifier robustness. Our method achieves significant gains over SOTA across multiple benchmarks: +12.7% in counterfactual validity, alongside consistent improvements in BLEU and BERTScore, with strong cross-LLM generalization.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The need for interpretability in deep learning has driven interest in counterfactual explanations, which identify minimal changes to an instance that change a model's prediction. Current counterfactual (CF) generation methods require task-specific fine-tuning and produce low-quality text. Large Language Models (LLMs), though effective for high-quality text generation, struggle with label-flipping counterfactuals (i.e., counterfactuals that change the prediction) without fine-tuning. We introduce two simple classifier-guided approaches to support counterfactual generation by LLMs, eliminating the need for fine-tuning while preserving the strengths of LLMs. Despite their simplicity, our methods outperform state-of-the-art counterfactual generation methods and are effective across different LLMs, highlighting the benefits of guiding counterfactual generation by LLMs with classifier information. We further show that data augmentation by our generated CFs can improve a classifier's robustness. Our analysis reveals a critical issue in counterfactual generation by LLMs: LLMs rely on parametric knowledge rather than faithfully following the classifier.

Problem

• Generating high-quality counterfactual explanations without task-specific fine-tuning.
• Improving label-flipping counterfactuals using classifier-guided LLM approaches.
• Enhancing classifier robustness through counterfactual data augmentation.

Innovation

• Classifier-guided LLMs for counterfactual generation
• Eliminates need for task-specific fine-tuning
• Improves classifier robustness via data augmentation