Research· 🧪 Worth Testing
Optimizing Knowledge Editing in LLMs Through Sparse Target Regularization
Researchers have identified that the upstream construction of target hidden states significantly impacts the quality of LLM knowledge updates. By implementing an L1 regularization method to prune extraneous components of knowledge vectors, this approach reduces interference during parameter editing, allowing for more precise knowledge injection.