model editing

Model editing directly updates specific weights to fix factual errors or modify behaviors without full retraining. **Motivation**: Models contain factual errors, knowledge becomes outdated, want to fix specific behaviors. Full retraining expensive and may lose capabilities. **Approaches**: **Locate-then-edit**: Find neurons/parameters responsible for fact, update those weights. **Hypernetwork**: Train network to predict weight updates for edits. **ROME/MEMIT**: Rank-one model editing in MLP layers where factual associations stored. **Edit types**: Factual updates ("The president of X is now Y"), behavior changes, bias corrections. **Evaluation criteria**: **Efficacy**: Does edit work? **Generalization**: Does it work for rephrasings? **Specificity**: Are unrelated facts preserved? **Challenges**: Edits may break model coherence, ripple effects on related knowledge, scalability to many edits. **Tools**: EasyEdit, PMET, custom implementations. **Alternatives**: RAG with updated knowledge base (avoids editing model), fine-tuning on corrections. **Use cases**: Recent news updates, correcting misinformation, personalizing responses. Active research area for maintaining LLM accuracy.