MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA
This addresses the need for efficient and versatile model editing in NLP, offering a solution that is incremental but improves upon existing methods by supporting multiple editing properties with reduced resource requirements.
The paper tackles the problem of efficiently updating large language models after deployment to fix errors or incorporate new knowledge, proposing MELO, a plug-in model editing method that achieves state-of-the-art performance on tasks like document classification, question answering, and hallucination correction with minimal trainable parameters and computational cost.
Large language models (LLMs) have shown great success in various Natural Language Processing (NLP) tasks, whist they still need updates after deployment to fix errors or keep pace with the changing knowledge in the world. Researchers formulate such problem as Model Editing and have developed various editors focusing on different axes of editing properties. However, current editors can hardly support all properties and rely on heavy computational resources. In this paper, we propose a plug-in Model Editing method based on neuron-indexed dynamic LoRA (MELO), which alters the behavior of language models by dynamically activating certain LoRA blocks according to the index built in an inner vector database. Our method satisfies various editing properties with high efficiency and can be easily integrated into multiple LLM backbones. Experimental results show that our proposed MELO achieves state-of-the-art editing performance on three sequential editing tasks (document classification, question answering and hallucination correction), while requires the least trainable parameters and computational cost.