ReLearn: Learning new things for Large Language Models
Summary
The paper “ReLearn: Unlearning via Learning for Large Language Models” presents a novel method for unlearning in LLMs while preserving fluency and relevance. It introduces a data augmentation and fine-tuning pipeline as an alternative to ‘gradient ascent (GA)’ and ’negative preference optimization (NPO]’, which degrade linguistic coherence.
How to Implement This Paper
To implement ReLearn, we will follow these key steps:
1️⃣ Understanding the Core Approach
- Data Augmentation: Generate diverse question-answer (QA) variations for forgetting while ensuring non-sensitive yet relevant responses.
- Fine-Tuning: Replace the knowledge to be forgotten with relevant but non-sensitive responses.
- Evaluation Metrics: Use the paper’s Knowledge Forgetting Rate (KFR), Knowledge Retention Rate (KRR), and Linguistic Score (LS) for performance assessment.
2️⃣ Setting Up the Development Environment
We need: