Paper Overview

Core Contribution

• UPCORE: A method-agnostic data selection framework for mitigating collateral damage during unlearning in large language models (LLMs).
• Key Insight: Model damage during unlearning is correlated with the variance of the model’s representations on the forget set. UPCORE selectively prunes outliers to minimize degradation.
• Innovation: Introduces a new metric, AUC (area-under-the-curve), to evaluate the trade-off between deletion efficacy and model preservation.

Research Context

• Problem: Unlearning specific data points from pretrained models often degrades performance on unrelated data, leading to a trade-off between deletion and model utility.
• Motivation: Legal frameworks (e.g., GDPR, CCPA) require the removal of sensitive data, but current unlearning methods struggle to balance deletion and model preservation.
• Prior Work: Existing unlearning methods (e.g., Gradient Ascent, Refusal, NPO) often lead to unintended consequences, such as reduced model utility.

Keywords

• Machine Unlearning, Coreset Selection, Utility Preservation, Collateral Damage, Isolation Forest, AUC Metric, LLMs, Data Privacy

Background

Research Gap

• Challenge: Existing unlearning methods do not systematically control data attributes (e.g., variance) that drive collateral damage.
• Question: What measurable attributes of the forget set drive collateral effects, and can they be controlled to optimize the trade-off between deletion and utility?

Technical Challenges

• Overgeneralization: Unlearning updates often propagate beyond the forget set, affecting unrelated data.
• Model Degradation: Unlearning can lead to significant performance loss on retained data.
• Data Variance: High variance in the forget set correlates with increased collateral damage.

Prior Approaches

• Exact Unlearning: Ensures the model is indistinguishable from one retrained without the forget data (computationally expensive).
• Approximate Unlearning: Modifies model parameters to approximate exact unlearning (more efficient but less precise).
• Model Editing: Directly modifies model weights to forget specific facts, but often leads to unintended consequences.

Methodology

Technical Architecture

1. Hidden State Extraction: Extract hidden state representations from the model’s penultimate layer for each data point in the forget set.
2. Isolation Forest Training: Train an Isolation Forest model to identify outliers in the forget set based on hidden state variance.
3. Coreset Selection: Prune outliers to form a core forget set with lower variance.
4. Unlearning: Apply unlearning algorithms (e.g., Gradient Ascent, Refusal, NPO) to the core forget set.

Implementation Details

• Isolation Forest: An unsupervised learning technique that efficiently identifies anomalous data points by recursively partitioning the dataset.
• Anomaly Score: Computed for each data point, with higher scores indicating greater contribution to variance.
• Pruning Threshold: Determined by either coreset size control or proportional pruning.

Innovation Points

• Variance Minimization: Reduces collateral damage by pruning high-variance outliers.
• Positive Transfer: Leverages overgeneralization to extend unlearning to pruned points.
• Method-Agnostic: Can be applied to any data-driven unlearning framework.

Results

Experimental Setup

• Unlearning Methods: Gradient Ascent, Refusal, Negative Preference Optimization (NPO).
• Datasets: Counterfact (prompt completion) and TriviaQA (question answering).
• Metrics: ROUGE scores, model utility, AUC for deletion effectiveness and utility retention.

Key Findings

• Superior Trade-off: UPCORE consistently achieves higher AUC across all unlearning methods, indicating a better balance between deletion and model utility.
• Positive Transfer: Unlearning on the core forget set extends to pruned points, reducing collateral damage.
• Robustness: UPCORE generalizes well to paraphrased and jailbreak prompts, maintaining high deletion efficacy.

Limitations

• Coreset Size: Performance gains stabilize beyond 30% pruning, but further pruning may degrade forget set performance.
• Scalability: The effectiveness of UPCORE may vary with the size and complexity of the forget set.

Conclusion

• UPCORE: A principled framework for utility-preserving unlearning in LLMs, leveraging variance minimization to reduce collateral damage.
• AUC Metric: Introduces a novel metric to quantify the trade-off between deletion and model utility across unlearning epochs.
• Future Work: Explore the application of UPCORE to other machine learning models and unlearning scenarios.

Acknowledgments

• Supported by NSF-CAREER Award 1846185, Microsoft AFMR, DARPA ECOLE, and NSF-AI Engage Institute.