Summary of Elder: Enhancing Lifelong Model Editing with Mixture-of-lora, by Jiaang Li et al.
ELDER: Enhancing Lifelong Model Editing with Mixture-of-LoRA
by Jiaang Li, Quan Wang, Zhongnan Wang, Yongdong Zhang, Zhendong Mao
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed ELDER approach addresses the limitations of existing model editing methods by creating a continuous association between data and adapters. This allows for robustness to minor input variations, enhancing edit efficiency and generalization. To achieve this, ELDER integrates multiple LoRAs through a router network and trains a novel loss function to guide the model’s link allocation with edit knowledge. The approach also includes a deferral mechanism to retain original LLM capabilities post-edit. Experimental results on GPT-2 XL and LLaMA2-7B demonstrate ELDER’s effectiveness in lifelong editing scenarios, outperforming eight baselines while preserving LLM general abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ELDER is a new way to update large language models (LLMs) so they can learn from small changes over time. Right now, most ways to edit LLMs are only good for one-time use and forget what they learned before. ELDER fixes this by making a connection between the data it sees and the adapters that help it understand that data. This makes the edits more robust and generalizable. To make sure the same knowledge is processed in the same way, ELDER uses a special loss function to guide its decisions. It also has a mechanism to keep the original LLM abilities after editing. Tests on GPT-2 XL and LLaMA2-7B show that ELDER works well for lifelong learning and preserves the LLM’s overall capabilities. |
Keywords
» Artificial intelligence » Generalization » Gpt » Loss function
