Resolving Editing-Unlearning Conflicts: A Knowledge Codebook Framework for Large Language Model Updating

Understand

Large Language Models (LLMs) excel in natural language processing by encoding extensive human knowledge, but their utility relies on timely updates as knowledge evolves.

• Updating LLMs involves two key tasks simultaneously: unlearning to remove unwanted knowledge and editing to incorporate new information.
• Existing methods face two major challenges: ineffective knowledge storage (either too sparse or too dense) and task conflicts between editing and unlearning, as validated through our theoretical and experimental results.
• To address these issues, we propose LOKA, a conflict-free framework for LLM updating based on a knowledge codebook.