A research project investigating machine unlearning techniques for selectively removing specific data from fine-tuned language models without full retraining.
- Fine-tuning small LLMs on biographical QnA datasets
- Selective removal of 2% of training data while preserving accuracy
- Parameter-efficient methods using LoRA for computational efficiency
- Comparison of existing unlearning techniques with improved baselines
- Focus on privacy-preserving AI and data governance applications
More detailed information and technical implementation coming soon.
Machine unlearning addresses a critical challenge in modern AI: how do you remove specific information from a trained model without starting from scratch? This becomes essential for privacy compliance, correcting misinformation, or removing outdated data from deployed systems.
Traditional approaches require complete retraining, which is computationally expensive and often impractical for large models. Our research explores efficient alternatives that can selectively "forget" targeted information while maintaining model performance on remaining data.
To learn more about this ongoing research, check back soon for detailed technical insights and implementation details.